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1. Assessment of Long-term Hydrologic Flux variation in five critically ground water depleted districts of Central Punjab, India

Author: Manish Debnath1*, Arjamadutta Sarangi2, Dipaka Ranjan Sena 1Ph.D. Research Scholar, Division Of Agricultural Engineering, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, & Scientist, ICAR-National Rice Research Institute, Cuttack. Email: Debnathmanish55@Gmail.Com 2Principal Scientist, Water Technology Centre, ICAR-IARI, New Delhi. Email: Arjamadutta.Sarangi@Mail.Mcgill.Ca 3Principal Scientist, Division Of Agricultural Engineering, ICAR-IARI, New Delhi. Email: Drsena_icar@Yahoo.Co.In
Abstract: Long-term variation of hydrological fluxes from 1984-2013 (30 years) over an ungauged watershed comprising, Barnala, Moga, Patiala, Sangrur and Ludhiana districts of Punjab state, India was undertaken. Validated SWAT model using the actual evapotranspiration data derived through operational simplified surface energy balance (SSEBop) model version 5.0 for the study region was used for sub-basin wise and district wise long-term hydrological flux estimation. The 30 years average percolation rate (PERC), actual evapotranspiration (AET), Surface runoff (SurQ) and water yield (WYLD) values were estimated as 7.0 %, 72%, 21.2 % and 21.7 % over the average rainfall (RF), respectively for all the sub-basins falling inside the study districts. The highest yearly average AET was estimated for Barnala district (86.1% of district average RF), whereas the lowest AET was estimated for Patiala districts (66.7% of district average RF). The highest yearly average WYLD value was estimated for Patiala district (26.8 % of the district average RF) and the lowest WYLD was found to be 10.6 % of the district average RF for Moga district. The highest PERC rate was found for Patiala district (8.6% of the district average RF) and the lowest PERC was found to be 1.8 % of the district average RF for Moga district. Moreover, the AET values were found more in non-monsoon period (ranged from 129.5%-141% of district RF) compared to monsoon period (10.2%-71.4% of district RF). On the contrary, the PERC and WYLD values were found less in non-monsoon period (varying from 0.2%-2.5% and 5.6%-10.9% of district RF, respectively) compared to the monsoon period (varying from 2.9% -10.2% and 13.1%- 28.6% of district RF, respectively). The estimated hydrologic fluxes can be used for judicious irrigation planning and sustainable uses of ground water resources in the study districts cultivating rice-wheat as the major crops. Keywords: Ungauged watershed, Central Punjab, Actual Evapotranspiration, hydrological fluxes, irrigation planning
Published: 2022
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2. C-MIP: An international model inter-comparison simulating organic carbon dynamics in bare fallow soils

Author: Farina, Roberta, Ehrhardt, Fiona, Bellocchi, Gianni, Chenu, Claire, Soussana, Jean-François, Abdalla, Mohamed, Álvaro-Fuentes, Jorge, Blauer, Mira, Brilli, Lorenzo, Chakrabarti, Bidisha, CLIVOT, Hugues, De Antoni, Max, di bene, claudia, Dorich, Cristopher, Ferchaud, Fabien, Nuala, Fitton, Francaviglia, Rosa, Franko, Uwe, Grant, Brian B, Guenet, Bertrand, Harrison, Matthew T., Kirschbaum, Miko U.F., Kuka, Katrin, Lehtonen, Aleksi, Martin, Raphaël, Meier, Elizabeth Anne, Menichetti, Lorenzo, Mula, Laura, Nendel, Claas, Rolinski, Susanne, Sharp, Joanna, Shepherd, Anita, Smith, Ward, Snow, Val, Taghizadeh-Toosi, Arezoo, Tsutskikh, Elena, Zhang, Qing, Recous, Sylvie, Research Centre for the Soil-Plant System, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Collège de Direction, Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Institute of Biological and Environmental Sciences, (SFIRC), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Consejo Superior de Investigaciones Científicas, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Indian Agricultural Research Institute (IARI), UR 1158 AgroImpact Agroressources et Impacts environnementaux, Queensland University of Technology, Colorado State University [Fort Collins] (CSU), Helmholtz Centre for Environmental Research (UFZ), Agriculture Canada, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Tasmanian Institute of Agriculture, Landcare Research, Julius Kühn-Institut (JKI), Natural Resources Institute Finland, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Swedish University of Agricultural Sciences (SLU), Dipartimento di Agraria e Nucleo Ricerca Desertificazione, Università degli Studi di Sassari, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), The New Zealand Institute for Plant and Food Research, Rothamsted Research, Agriculture and Agri-Food [Ottawa] (AAFC), AgResearch Ltd, Aarhus University, Institute of Atmospheric Physics [Beijing] (IAP), Chinese Academy of Sciences [Beijing] (CAS), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria ( CREA ), Institut National de la Recherche Agronomique ( INRA ), Unité de Recherche sur l'Ecosystème Prairial, ( SFIRC ), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes ( ECOSYS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Istituto di Biometeorologia [Firenze] ( IBIMET ), Consiglio Nazionale delle Ricerche ( CNR ), Indian Agricultural Research Institute ( IARI ), Colorado State University, Helmholtz Centre for Environmental Research ( UFZ ), LSCE, Centre National de la Recherche Scientifique ( CNRS ), Julius Kühn-Institut ( JKI ), Commonwealth Scientific and Industrial Research Organisation, Swedish University of Agricultural Sciences ( SLU ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Agriculture and Agri-Food Canada ( AAFC ), Institute of Atmospheric Physics [Beijing] ( IAP ), Chinese Academy of Sciences [Beijing] ( CAS ), Fractionnement des AgroRessources et Environnement - UMR-A 614 ( FARE ), Université de Reims Champagne-Ardenne ( URCA ) -Institut National de la Recherche Agronomique ( INRA ) -SFR Condorcet, and Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS )
Subjects: [ SDV ] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract: C-MIP: An international model inter-comparison simulating organic carbon dynamics in bare fallow soils. 6th International Symposium on Soil Organic Matter
Published: 2017

3. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author: Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), CSIRO, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: Crops, Agricultural, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Water en Voedsel, Growing season, Climate change, klim, Plant Science, Agricultural engineering, Models, Biological, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Life Science, Computer Simulation, Productivity, 0105 earth and related environmental sciences, 2. Zero hunger, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Water and Food, Food security, business.industry, Crop yield, Temperature, Agriculture, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Agronomy, Klimaatbestendigheid, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.
Published: 2017
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4. Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

Author: Yulong Wang, Hiroe Yoshida, Liang Tang, Manuel Marcaida, Yuji Masutomi, Donald S. Gaydon, Roberto Confalonieri, Kenneth J. Boote, Hiroshi Nakagawa, Fulu Tao, Philippe Oriol, Lloyd T. Wilson, Yan Zhu, Samuel Buis, Simone Bregaglio, Xinyou Yin, Jeffrey T. Baker, Soora Naresh Kumar, Françoise Ruget, Lianxin Yang, Jianguo Zhu, Job Fugice, Yubin Yang, Upendra Singh, Tao Li, Toshihiro Hasegawa, Zhao Zhang, Tanguy Lafarge, Hitomi Wakatsuki, Daniel Wallach, Tamon Fumoto, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization (NARO), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Centre for Crop Systems Analysis, Wageningen University and Research [Wageningen] (WUR), National Engineering and Technology Center for Information Agriculture, China Agricultural University (CAU), Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, University of Florida [Gainesville] (UF), USDA-ARS : Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cassandra laboratory, University of Milan, International Fertilizer Development Center (IFDC), Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Indian Agricultural Research Institute (IARI), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut National de la Recherche Agronomique (INRA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), College of Agriculture, Northeast Agricultural University [Harbin], Muscle Shoals, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Natural resources institute Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Yangzhou University, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences, National Agriculture and Food Research Organization, International Rice Research Institute, Wageningen University and Research Centre [Wageningen] (WUR), Chinese Agricultural University, University of Florida [Gainesville], United States Department of Agriculture - Agricultural Research Service, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Natural Resources Institute Finland, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Beijing Normal University, Wageningen University and Research Centre [Wageningen] ( WUR ), University of Florida, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria ( CREA ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Avignon et des Pays de Vaucluse ( UAPV ), International Fertilizer Development Center ( IFDC ), Commonwealth Scientific and Industrial Research Organisation, Indian Agricultural Research Institute ( IARI ), UMR AGAP, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, AGAP, Université de Montpellier ( UM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de Recherche en Informatique et en Automatique ( Inria ), Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Chinese Academy of Sciences ( CAS ), and Texas A and M AgriLIFE Research Center at Beaumont
Subjects: 010504 meteorology & atmospheric sciences, lcsh:Medicine, variabilité du rendement, diffusion de co2, 01 natural sciences, F50 - Anatomie et morphologie des plantes, Productivité, modèle de culture, F01 - Culture des plantes, Photosynthèse, lcsh:Science, Milieux et Changements globaux, riz, 2. Zero hunger, Multidisciplinary, élément fertilisant, Ecology, food and beverages, Feuille, Surface foliaire, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Pratique culturale, Variation (linguistics), Rendement des cultures, Crops, Agricultural, Crop Physiology, Nitrogen, Climate Change, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, F60 - Physiologie et biochimie végétale, Climate change, Soil science, Teneur en azote, Models, Biological, Article, Fertilisation, growth chambers, Life Science, chambre de croissance, Management practices, 0105 earth and related environmental sciences, atmospheric carbon-dioxide, climate change, elevated CO2, environmental variation, leaf-area, oryza-sativa l, simulation model, seasonal change, crop production, biomass growth, Morphologie végétale, Méthode statistique, lcsh:R, Oryza, Modèle de simulation, Carbon Dioxide, Plant Leaves, F61 - Physiologie végétale - Nutrition, 13. Climate action, émission d'azote, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, lcsh:Q, adaptation au changement climatique, Cycle du carbone
Abstract: The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.
Published: 2017
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5. A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands

Author: Renata Sandor, Fiona Ehrhardt, Bruno Basso, Gianni Bellocchi, Arti Bhatia, Lorenzo Brilli, Massimiliano de Antoni Migliorati, Jordi Doltra, Chris Dorich, Luca Doro, Nuala Fitton, Sandro José Giacomini, Peter Grace, Brian Grant, Harrison, Matthew T., Jones, Stephanie K., Kirschbaum, Miko U. F., Katja Klumpp, Patricia Laville, Joël Léonard, Mark Liebig, Mark Lieffering, Raphaël Martin, Russel Mcauliffe, Elizabeth Meier, Lutz Merbold, Moore, Andrew D., Vasileios Myrgiotis, Paul Newton, Elizabeth Pattey, Sylvie Recous, Susanne Rolinski, Joanna Sharp, Raia Silvia Massad, Pete Smith, Ward Smith, Val Snow, Lianhai Wu, Wen Zhang, Jean-François Soussana, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Collège de Direction, Institut National de la Recherche Agronomique (INRA), Michigan State University [East Lansing], Michigan State University System, Indian Agricultural Research Institute (IARI), University of Florence (UNIFI), Queensland University of Technology, Cantabrian Agricultural Research and Training Centre, Colorado State University [Fort Collins] (CSU), University of Sassari, University of Aberdeen, Federal University of Santa Maria, Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SCUR), Landcare Research, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), United States Department of Agriculture - Agricultural Research Service, AgResearch Ltd, CSIRO, International Livestock Research Institute, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), AgResearch Grasslands, Agricuture and Agri-Food Canada, Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Potsdam Institute for Climate Impact Research (PIK), New Zealand Institute for Plant and Food Research Limited, Rothamsted Research, Chinese Academy of Sciences (CAS), CN-MIP, Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Unité de Recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique ( INRA ), Michigan State University, Indian Agricultural Research Institute ( IARI ), University of Florence, Colorado State University, Agriculture and Agri-Food Canada ( AAFC ), Scotland's Rural College, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes ( ECOSYS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Unité d'Agronomie de Laon-Reims-Mons ( AGRO-LRM ), Commonwealth Scientific and Industrial Research Organisation, Fractionnement des AgroRessources et Environnement - UMR-A 614 ( FARE ), Université de Reims Champagne-Ardenne ( URCA ) -Institut National de la Recherche Agronomique ( INRA ) -SFR Condorcet, Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS ), Potsdam Institute for Climate Impact Research ( PIK ), Chinese Academy of Sciences ( CAS ), Collège de Direction (CODIR), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), and Plant & Food Research
Subjects: modelling, field crop, [ SDV ] Life Sciences [q-bio], grandes cultures et prairies, carbon, [SDV]Life Sciences [q-bio], prairie, grande culture, carbone, grassland, modélisation
Abstract: A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands. 6th International Symposium on Soil Organic Matter ; Harpenden (Royaume Uni)
Published: 2017

6. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author: Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)
Subjects: 0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, régression statistique, 010504 meteorology & atmospheric sciences, impact sur le rendement, klim, Atmospheric sciences, 01 natural sciences, incertitude, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, changement climatique, Regression analysis, statistical regression, simulation, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, sécurité alimentaire, Plant Production Systems, modèle de récolte, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, Environmental Science (miscellaneous), Earth System Science, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, réchauffement climatique, global change, 0105 earth and related environmental sciences, Hydrology, WIMEK, Global temperature, business.industry, Crop yield, Global warming, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, business, Social Sciences (miscellaneous), 010606 plant biology & botany
Abstract: The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
Published: 2016
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7. Multi-wheat-model ensemble responses to interannual climate variability

Author: Carlos Angulo, Frank Ewert, Tom M. Osborne, Pramod K. Aggarwal, Senthold Asseng, Pasquale Steduto, Kurt Christian Kersebaum, Eckart Priesack, Patrick Bertuzzi, Roberto C. Izaurralde, Dominique Ripoche, Thilo Streck, Joost Wolf, Pierre Stratonovitch, Alex C. Ruane, Richard Goldberg, Robert F. Grant, Taru Palosuo, Iurii Shcherbak, Kenneth J. Boote, Christian Biernath, Garry O'Leary, J. Hooker, Peter J. Thorburn, Joachim Ingwersen, Soora Naresh Kumar, Lee Heng, Maria I. Travasso, Pierre Martre, Katharina Waha, Nicholas I. Hudson, Claas Nendel, Fulu Tao, Christoph Müller, Andrew J. Challinor, Jørgen E. Olesen, Reimund P. Rötter, Davide Camarrano, L. A. Hunt, Sebastian Gayler, Nadine Brisson, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Iwan Supit, Jordi Doltra, Jeffrey W. White, Bruno Basso, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Agricultural & Biological Engineering Department, University of Florida [Gainesville], The James Hutton Institute, Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Research Program on Climate Change, Agriculture and Food Security, International Water Management Institute, International Water Management Institute, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Cantabrian Agricultural Research and Training Centre, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University, National Centre for Atmospheric Science, Department of Meteorology, Environmental Impacts Group, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Institute for Future Environments, Queensland University of Technology, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Earth System Science-Climate Change and Adaptive Land-use and Water Management, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water [Castelnar], Instituto Nacional de Tecnología Agropecuaria (INTA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, USDA-ARS, Arid-Land Agricultural Research Center, United States Department of Agriculture, Plant Production Systems, Modelling European Agriculture with Climate Change for Food Security (MACSUR), JPI FACCE, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Agroclim (AGROCLIM), Aarhus University [Aarhus], Natural resources institute Finland, Georg-August-University [Göttingen], Wageningen University and Research [Wageningen] (WUR), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Biotechnology and Biological Sciences Research Council (BBSRC), Queensland University of Technology [Brisbane] (QUT), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Center for Climate Systems Research [New York] ( CCSR ), University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Leibniz Centre for Agricultural Landscape Research (ZALF), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Commonwealth Scientific and Industrial Research Organisation, Department of Geological Sciences and Kellogg Biological Station, Michigan State University, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), University of Maryland, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Potsdam Institute for Climate Impact Research ( PIK ), Washington State University ( WSU ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Institute for Climate and Water, and Instituto Nacional de Tecnología Agropecuaria
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Temperature sensitivity, 010504 meteorology & atmospheric sciences, Precipitation, 01 natural sciences, modèle de croissance, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, climate impacts, 2. Zero hunger, Multi-model ensemble, changement climatique, Ecological Modeling, Temperature, Uncertainty, 04 agricultural and veterinary sciences, PE&RC, Plant Production Systems, Climatology, PRECIPITATION, Climate impacts, Environmental Engineering, interannual variability, Yield (finance), australie, variation interannuelle, Growing season, Climate change, multi-model ensemble, Earth System Science, Interannual variability, blé, température, pays bas, global change, 0105 earth and related environmental sciences, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, précipitation, business.industry, argentine, Simulation modeling, temperature, Global change, 15. Life on land, inde, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Crop modeling, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Software
Abstract: We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2???0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.
Published: 2016
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8. Multimodel ensembles of wheat growth: many models are better than one

Author: Pierre Martre, Bruno Basso, James W. Jones, Jordi Doltra, Garry O'Leary, Pramod K. Aggarwal, Christian Biernath, Jeffrey W. White, Sebastian Gayler, R. Goldberg, Eckart Priesack, Robert F. Grant, Nadine Brisson, Patrick Bertuzzi, Thilo Streck, Daniel Wallach, Joachim Ingwersen, Davide Cammarano, J. Hooker, Fulu Tao, Christoph Müller, Carlos Angulo, Soora Naresh Kumar, Claas Nendel, Jørgen E. Olesen, Lee Heng, Maria I. Travasso, Iurii Shcherbak, Mikhail A. Semenov, Claudio O. Stöckle, Tom M. Osborne, L. A. Hunt, Alex C. Ruane, Frank Ewert, Kenneth J. Boote, Andrew J. Challinor, Reimund P. Rötter, Iwan Supit, Jerry L. Hatfield, Roberto C. Izaurralde, Senthold Asseng, Cynthia Rosenzweig, Pasquale Steduto, Kurt Christian Kersebaum, Dominique Ripoche, Peter J. Thorburn, Pierre Stratonovitch, Joost Wolf, Katharina Waha, Taru Palosuo, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Research, Agrifood Research Finland, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), National Laboratory for Agriculture and Environment, Consultative Group on International Agricultural Research, Research Program on ClimateChange, Agriculture and Food Security, International Water Management Institute, Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, German Research Center for Environmental Health, Helmholtz-Zentrum München (HZM), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Cantabrian Agricultural Research and Training Centre, Water & Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), School of Agriculture, Policy and Development, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, Universität Stuttgart, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Primary Industries, Landscape & Water Sciences, Université Paris Diderot - Paris 7 (UPD7), Department of Agroecology, Aarhus University [Aarhus], National Centre for Atmospheric Science, Department of Meteorology, Institute of Soil Ecology German Research Center for Environmental Health, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization of the United Nations, Washington State University (WSU), University of Hohenheim, Wageningen University and Research Centre [Wageningen] (WUR), Institute Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), CIRN, Institute forClimate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Arid-Land Agricultural Research Center, United States Department of Agriculture, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn], Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Institut National de la Recherche Agronomique ( INRA ), Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Kellogg Biological Station, UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, School of Earth and Environment [Leeds] ( SEE ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: Calibration (statistics), Climate, Statistics, process-based model, grain, uncertainty, Triticum, General Environmental Science, Mathematics, 2. Zero hunger, Global and Planetary Change, Ecology, Mathematical model, Estimator, PE&RC, simulation, ecophysiological model, Europe, [ SDE.MCG ] Environmental Sciences/Global Changes, model intercomparison, Plant Production Systems, Wheat (Triticum aestivum L.), climate-change, wheat (Triticum aestivum L.), Centre for Crop Systems Analysis, impact, Seasons, simulations, europe, ensemble modeling, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, australia, crop production, Environment, Models, Biological, Consistency (statistics), Approximation error, Environmental Chemistry, Alterra - Centrum Bodem, impacts, Hydrology, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Ensemble forecasting, Crop yield, Simulation modeling, Soil Science Centre, Australia, yield, calibration, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Plantaardige Productiesystemen, biodiversity conservation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences, billion
Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
Published: 2015
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9. Statistical analysis of large simulated yield datasets for studying climate change effects

Author: David Makowski, Senthold Asseng, Frank Ewert, Simona Bassu, Jean-Louis Durand, Pierre Martre, Myriam Adam, Pramod K. Aggarwal, Carlos Angulo, Christian Baron, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Hendrik Boogaard, Kenneth J. Boote, Nadine Brisson, Davide Cammarano, Andrew J. Challinor, Sjakk J. G. Conijn, Marc Corbeels, Delphine Deryng, Giacomo De Sanctis, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Patricio Grassini, Jerry L. Hatfield, Lee Heng, Steven Hoek, Josh Hooker, Tony L. A. Hunt, Joachim Ingwersen, Cesar Izaurralde, Raymond E. E. Jongschaap, James W. Jones, Armen R. Kemanian, Christian Kersebaum, Soo-Hyung Kim, Jon Lizaso, Christoph Müller, Naresh S. Kumar, Claas Nendel, Garry J. O'Leary, Jorgen E. Olesen, Tom M. Osborne, Taru Palosuo, Maria V. Pravia, Eckart Priesack, Dominique Ripoche, Cynthia Rosenzweig, Alexander C. Ruane, Fredirico Sau, Mickhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Edmar I. Teixeira, Peter Thorburn, Denis Timlin, Maria Travasso, Reimund Rötter, Katharina Waha, Daniel Wallach, Jeffrey W. White, Jimmy R. Williams, Joost Wolf, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, University of Florida [Gainesville] (UF), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Michigan State University [East Lansing], Michigan State University System, Agroclim (AGROCLIM), German Research Center for Environmental Health, Centre for Geo-Information, University of Leeds, International Center for Tropical Agriculture, Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Sciences [Changchun Branch] (CAS), University of East Anglia, Catabrian Agricultural Research and Training Center (CIFA), University of Tübingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), University of Nebraska [Lincoln], University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), University of Reading (UOR), University of Guelph, University of Hohenheim, Joint Global Change Research Institute, Instituto Nacional de Investigación Agropecuaria (INIA), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), Department of Environment and Primary Industries, Landscape and Water Sciences, Aarhus University [Aarhus], Agrifood Research Finland, Pennsylvania State University (Penn State), Penn State System, Rothamsted Research, FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Washington State University (WSU), Plant & Food Research, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS : Agricultural Research Service, Institute for Climate and Water, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Hillel, D., Rosenzweig, C., Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Florida, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Université Clermont Auvergne ( UCA ), Université Blaise Pascal (Clermont Ferrand 2) ( UBP ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Michigan State University, UE Agroclim ( UE AGROCLIM ), Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), Catabrian Agricultural Research and Training Center ( CIFA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska Lincoln ( UNL ), International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Indian Agricultural Research Institute ( IARI ), Aarhus University, PennState University [Pennsylvania] ( PSU ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), New Zealand Institute for Plant and Food Research Limited, Commonwealth Scientific and Industrial Research Organisation, United States Department of Agriculture - Agricultural Research Service, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), AgroParisTech-Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Université Blaise Pascal (Clermont Ferrand 2) (UBP), UE Agroclim (UE AGROCLIM), Wageningen University and Research Center (WUR), Food and Agricultural Organization (FAO), Helmholtz Zentrum München = German Research Center for Environmental Health, University of East Anglia [Norwich] (UEA), University of Nebraska–Lincoln, Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: analyse de données, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Earth Observation and Environmental Informatics, 010504 meteorology & atmospheric sciences, Yield (finance), data analysis, Climate change, 01 natural sciences, Agro Water- en Biobased Economy, statistical analysis, Effects of global warming, Aardobservatie en omgevingsinformatica, Life Science, Alterra - Centrum Bodem, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, global change, 0105 earth and related environmental sciences, 2. Zero hunger, changement climatique, WIMEK, Mathematical model, analyse statistique, Crop yield, Soil Science Centre, Global change, Statistical model, 04 agricultural and veterinary sciences, 15. Life on land, PE&RC, Climate resilience, Climate Resilience, Plant Production Systems, Klimaatbestendigheid, 13. Climate action, Plantaardige Productiesystemen, Climatology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
Published: 2015
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10. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author: Joost Wolf, Xinyou Yin, Pierre Martre, Zhengtao Zhang, H. K. Soo, Manuel Marcaida, Nadine Brisson, Patrick Bertuzzi, Soo-Hyung Kim, Yan Zhu, Roberto C. Izaurralde, L. A. Hunt, Maria I. Travasso, Christian Baron, James W. Jones, R.E.E. Jongschaap, T. Palosuo, Daniel Wallach, Jerry L. Hatfield, Christian Biernath, G. De Sanctis, Senthold Asseng, H. Yoshida, Donald S. Gaydon, Edmar Teixeira, Davide Cammarano, Alex C. Ruane, C. Nendel, T. Hasegawa, Thilo Streck, Garry O'Leary, Upendra Singh, Frank Ewert, Delphine Deryng, R. Goldberg, Bas A. M. Bouman, Peter J. Thorburn, Tao Li, Roberto Confalonieri, Myriam Adam, Jes Olesen, Reimund P. Rötter, Tamon Fumoto, Patricio Grassini, Joachim Ingwersen, Robert F. Grant, Katharina Waha, James Williams, Fulu Tao, Eckart Priesack, Pramod K. Aggarwal, Liang Tang, Sebastian Gayler, Jordi Doltra, L. Heng, Christoph Müller, J.G. Conijn, Iwan Supit, S. Naresh Kumar, Iurii Shcherbak, Jeffrey W. White, Hendrik Boogaard, Kenneth J. Boote, David Makowski, Federico Sau, Jean-Louis Durand, Mikhail A. Semenov, Claudio O. Stöckle, Marc Corbeels, Steven Hoek, Simone Bregaglio, Hiroshi Nakagawa, Philippe Oriol, Anthony Challinor, R. A. Kemanian, Carlos Angulo, Pasquale Steduto, Bruno Basso, Kurt Christian Kersebaum, Cynthia Rosenzweig, Dennis Timlin, J. Hooker, Samuel Buis, Maria Virginia Pravia, Françoise Ruget, Dominique Ripoche, Simona Bassu, Pierre Stratonovitch, Jon I. Lizaso, Balwinder Singh, Tom M. Osborne, Paul W. Wilkens, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Int Rice Res Inst, Los Banos, Philippines, Université Paris Diderot - Paris 7 (UPD7), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), International Water Management Institute, Research Program on Climate Change, Agriculture and Food Security, CGIAR, Institute of Crops Science and Resource Conservation INRES, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Geological Sciences [East Lansing], Agroclim (AGROCLIM), German Research Center for Environmental Health, Institute of Soil Ecololgy, Helmholtz-Zentrum München (HZM), Center for Geo-information, Alterra, Department of Agronomy, University of Florida [Gainesville] (UF), Cassandra Lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, CGIAR ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, School of Earth and Environment [Leeds] (SEE), University of Leeds, Plant Research International, Wageningen University and Research [Wageningen] (WUR), Embrapa Cerrados, Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia [Norwich] (UEA), European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Tsukuba, National Institute of Agro-Environmental Sciences (NIAES), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), WESS Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Departement of Renewable Resources, University of Alberta, Department of Agronomy and Horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, National Laboratory for Agriculture and Environment, International Atomic Energy Agency [Vienna] (IAEA), Centre for Geo-Information, Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Instituto Nacional de Investigación Agropecuaria (INIA), Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), College of the Environment, School of Environmental and Forest Sciences, University of Washington, Department Produccion Vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Potsdam Institute for Climate Impact Research (PIK), National Agriculture and Food Research Organization (NARO), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Department of Economic Development Jobs, Transport and Resources, Grains Innovation Park, Department of Agroecology, Aarhus University [Aarhus], Walker Institute, NCAS Climate, Natural Resources Institute Finland, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, German Research Center for Environmental Health, Institute of Soil Ecology, Department Biologia Vegetal, Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences and W.K. Kellogg Biological Station, International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), International Fertilizer Development Center (IFDC), College of the Environment, School of Environmental and Forest Science, University of Washington [Seattle], FAO Sub-regional Office for Eastern Africa [Addis Ababa, Ethiopie] (FAO), Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Biological Systems Engineering, Washington State University (WSU), Plant Production Systems and Earth System Science, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, CIRN, Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agriculture, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Arid-Land Agricultural Research Center, Texas AgriLife Research and Extension, Texas A&M University System, Centre for Crop Systems Analysis, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), Metaprogramme ACCAF, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Università degli Studi di Milano = University of Milan (UNIMI), University of Nebraska–Lincoln, Université de Toulouse (UT)-Université de Toulouse (UT), Natural Resources Institute Finland (LUKE), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Nanjing Agricultural University (NAU), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation INRES, International Rice Research Institute, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Eberhard Karls Universität Tübingen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), National Agriculture and Food Research Organization, International Maize and Wheat Improvement Centre (CIMMYT), Food and Agricultural Organization (FAO), New Zealand Institute for Plant and Food Research Limited, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Beijing Normal University, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, University of Bonn (Rheinische Friedrich-Wilhelms), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ) -AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Department of Geological Sciences, W.K. Kellogg Biological Station, Michigan State Univ, Dept Geol Sci, E Lansing, MI 48823 USA, UE Agroclim ( UE AGROCLIM ), Helmholtz-Zentrum München ( HZM ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), Invergowrie, School of Earth and Environment [Leeds] ( SEE ), Wageningen University and Research Centre [Wageningen] ( WUR ), Agro-ecologyand Sustainable Intensification of Annual Crops, Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), University of East Anglia [Norwich] ( UEA ), European Commission - Joint Research Centre [Ispra] ( JRC ), National Institute for Agro-Environmental Sciences, Commonwealth Scientific and Industrial Research Organisation, NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Nebraska-Lincoln, International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), UMR 1248 Agrosystèmes et Développement Territorial (AGIR), Agro-ecology and Sustainable Intensification of Annual Crops, Instituto Nacional de Investigación Agropecuaria, Leibniz Centre for Agricultural Landscape Research, Universidad Politécnica de Madrid ( UPM ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), PennState University [Pennsylvania] ( PSU ), W.K. Kellogg Biological Station, Department of Geological Sciences, International Maize and Wheat Improvement Centre ( CIMMYT ), International Fertilizer Development Center ( IFDC ), Food and Agricultural Organization ( FAO ), Washington State University ( WSU ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, and Texas A and M University ( TAMU )
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, F62 - Physiologie végétale - Croissance et développement, 01 natural sciences, Statistics, Aardobservatie en omgevingsinformatica, Climate change, Crop model, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, Triticum, Mathematics, 2. Zero hunger, Global and Planetary Change, Mathematical model, Air, Forestry, Regression analysis, 04 agricultural and veterinary sciences, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, Rendement des cultures, Plant Production Systems, Statistical model, Modèle mathématique, Atmosphère, Earth Observation and Environmental Informatics, Yield, Crop Physiology, P40 - Météorologie et climatologie, [SDE.MCG]Environmental Sciences/Global Changes, Oryza sativa, Zea mays, Earth System Science, Emulator, Agro Water- en Biobased Economy, Alterra - Centrum Bodem, Precipitation, Croissance, 0105 earth and related environmental sciences, Meta-model, Changement climatique, Hydrology, Modélisation des cultures, Crop yield, Simulation modeling, Soil Science Centre, 15. Life on land, Température, Laboratorium voor Phytopathologie, Climate Resilience, 13. Climate action, Klimaatbestendigheid, Yield (chemistry), Plantaardige Productiesystemen, Laboratory of Phytopathology, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leerstoelgroep Aardsysteemkunde, Plante de culture, Agronomy and Crop Science, Dioxyde de carbone
Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.
Published: 2015
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11. How do various maize crop models vary in their responses to climate change factors?

Author: Federico Sau, Sjaak Conijn, Delphine Deryng, Jean-Louis Durand, Katharina Waha, Edmar Teixeira, Iurii Shcherbak, R.E.E. Jongschaap, James W. Jones, Kenneth J. Boote, Maria Virginia Pravia, Jerry L. Hatfield, Alex C. Ruane, Christian Biernath, Patricio Grassini, H.L. Boogaard, Steven Hoek, K. Christian Kersebaum, Fulu Tao, Christian Baron, David Makowski, Claas Nendel, Sebastian Gayler, Dennis Timlin, Marc Corbeels, Christoph Müller, Nadine Brisson, Jon I. Lizaso, Naresh S. Kumar, Cynthia Rosenzweig, Simona Bassu, Armen R. Kemanian, Cesar Izaurralde, Bruno Basso, Giacomo De Sanctis, Myriam Adam, Soo-Hyung Kim, Eckart Priesack, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Department of agronomy, University of Florida [Gainesville], Department Produccion vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Department of agricultural and biological engineering, GISS Climate impacts group, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC)-NASA Goddard Space Flight Center (GSFC), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department crop systems, forestry and environmental sciences, University of Basilicata, Centre for Geo-Information, ALTERRA, WUR-Plant research international, Wageningen University and Research Centre [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (Cirad-Persyst-UPR 115 AIDA), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), School of Environmental Sciences [Norwich], UE Agroclim (UE AGROCLIM), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen, Department of agronomy and horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, Department of plant science, University of Pensylvania, Institute of Lanscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of environmental and forest sciences, University of Washington [Seattle], Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of landscape systems analysis, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères ( P3F ), Institut National de la Recherche Agronomique ( INRA ), Universidad Politécnica de Madrid ( UPM ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ) -NASA Goddard Space Flight Center ( GSFC ), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Wageningen University and Research Centre [Wageningen] ( WUR ), Annual cropping systems, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Tyndall Centre for climate change research and school of environmental sciences, University of East Anglia [Norwich] ( UEA ), UE Agroclim ( UE AGROCLIM ), Water and earth system science (WESS) competence cluster, University of Nebraska-Lincoln, Leibniz Centre for Agricultural Landscape Research, Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Chinese Academy of Sciences [Changchun Branch] ( CAS ), University of Florida [Gainesville] (UF), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Wageningen University and Research [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), and Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, nitrogen dynamics, Atmospheric sciences, maize, 01 natural sciences, Standard deviation, F01 - Culture des plantes, wheat, Aardobservatie en omgevingsinformatica, water-use efficiency, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, General Environmental Science, agriculture, 2. Zero hunger, Global and Planetary Change, elevated co2, Ecology, Geography, Phenology, U10 - Informatique, mathématiques et statistiques, Agricultura, 04 agricultural and veterinary sciences, simulation, Rendement des cultures, model intercomparison, climate change, CO2, Crop simulation model, Modèle mathématique, Crops, Agricultural, simulation-model, Earth Observation and Environmental Informatics, air co2 enrichment, P40 - Météorologie et climatologie, Agmip, Climate, Maize, Model Intercomparison, Simulation, Temperature, Uncertainty, Climate change, carbon-dioxide, Models, Biological, Zea mays, Agro Water- en Biobased Economy, Environmental Chemistry, Water-use efficiency, climate, 0105 earth and related environmental sciences, Changement climatique, systems simulation, Modélisation des cultures, Crop yield, Simulation modeling, Water, temperature, Modèle de simulation, Carbon Dioxide, yield, Température, Agronomy, 13. Climate action, Yield (chemistry), 040103 agronomy & agriculture, AgMIP, 0401 agriculture, forestry, and fisheries, Environmental science, Dioxyde de carbone
Abstract: Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Published: 2014
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12. Uncertainty in simulating wheat yields under climate change

Author: J. Hooker, Pramod K. Aggarwal, Joost Wolf, Pierre Martre, Iurii Shcherbak, L. A. Hunt, Kenneth J. Boote, L. Heng, James W. Jones, Jerry L. Hatfield, Katharina Waha, Christian Biernath, Iwan Supit, Eckart Priesack, Pasquale Steduto, S. Naresh Kumar, Davide Cammarano, Joachim Ingwersen, Kurt Christian Kersebaum, Fulu Tao, Christoph Müller, Jordi Doltra, Thilo Streck, Senthold Asseng, Alex C. Ruane, Jeffrey W. White, Roberto C. Izaurralde, Tom M. Osborne, Patrick Bertuzzi, Sebastian Gayler, Andrew J. Challinor, Taru Palosuo, Reimund P. Rötter, Jørgen E. Olesen, Peter J. Thorburn, Nadine Brisson, Mikhail A. Semenov, Claudio O. Stöckle, Maria I. Travasso, Daniel Wallach, James Williams, Garry O'Leary, Cynthia Rosenzweig, Carlos Angulo, Bruno Basso, R. Goldberg, Robert F. Grant, Frank Ewert, Dominique Ripoche, Pierre Stratonovitch, Claas Nendel, Agricultural and Biological Engineering Department, University of Florida [Gainesville], Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National Laboratory for Agriculture and Environment, Department of Agronomy, Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), CCAFS, IWMI, NASC Complex, DPS Marg, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, Centro de Investigaciòn y Formenta Agrario (CIFA), WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, nstitute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, University of Maryland [College Park], University of Maryland System-University of Maryland System, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape and Water Sciences, Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Institute of Soil Science and Land Evaluation, Plant Production Systems and Earth System Science-Climate Change, Wageningen University and Research Centre [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Arid-Land Agricultural Research Center, Texas A&M University System, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Florida [Gainesville] (UF), Agroclim (AGROCLIM), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut für Nutzpflanzenwissenschaften und Ressourcenschutz ( INRES ), University of Bonn (Rheinische Friedrich-Wilhelms), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Génétique Diversité et Ecophysiologie des Céréales ( GDEC ), Institut National de la Recherche Agronomique ( INRA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), Helmholtz-Zentrum München ( HZM ), Institute for Climate and Atmospheric Science [Leeds] ( ICAS ), University of Leeds, Centro de Investigaciòn y Formenta Agrario ( CIFA ), University of Alberta [Edmonton], International Atomic Energy Agency [Vienna] ( IAEA ), University of Reading ( UOR ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Washington State University ( WSU ), Wageningen University and Research Centre [Wageningen] ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Instituto Nacional de Tecnología Agropecuaria, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Texas A and M University ( TAMU ), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)
Subjects: 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, projection, crop production, adaptation, Environmental Science (miscellaneous), 01 natural sciences, Greenhouse effect, Uncertainty analysis, 0105 earth and related environmental sciences, 2. Zero hunger, model, [ SDV ] Life Sciences [q-bio], food, Simulation modeling, ensemble, temperature, 04 agricultural and veterinary sciences, 15. Life on land, Transient climate simulation, scenario, 13. Climate action, Greenhouse gas, Climatology, 040103 agronomy & agriculture, impact, 0401 agriculture, forestry, and fisheries, Environmental science, co2, Climate model, Crop simulation model, Social Sciences (miscellaneous)
Abstract: Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
Published: 2013
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13. ICTV Virus Taxonomy Profile: Nanoviridae

Author: Thomas, John E., Gronenborn, Bruno, Harding, Robert M., Mandal, Bikash, Grigoras, Ioana, Randles, John W., Sano, Yoshitaka, Timchenko, Tania, Vetten, H. Josef, Yeh, Hsin-Hung, Ziebell, Heiko, Ictv Report Consortium, null, Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Intéractions Plantes-Bactéries (PBI), Département Microbiologie (Dpt Microbio), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Queensland Alliance for Agriculture and Food Innovation (QAAFI), University of Queensland [Brisbane], Queensland University of Technology [Brisbane] (QUT), Indian Agricultural Research Institute (IARI), Université d'Évry-Val-d'Essonne (UEVE), University of Adelaide, Niigata University, Julius Kühn-Institut (JKI), 9 Taipei Agricultural Biotechnology Research Center, Academia Sinica, Taipei, 115, Taiwan, ROC, CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institute for Integrative Biology of the Cell [Gif-sur-Yvette] (I2BC), and Queensland University of Technology (Queensland University of Technology - Brisbane) (QUT)
Subjects: 0301 basic medicine, biology, [SDV]Life Sciences [q-bio], 030106 microbiology, Nanoviridae, biology.organism_classification, Genome, Virology, Zingiberales, 03 medical and health sciences, taxonomy, 030104 developmental biology, Genus, Plant virus, ICTV Report, Taxonomy (biology), Eudicots, Virus classification
Abstract: Nanoviridae is a family of plant viruses (nanovirids) whose members have small isometric virions and multipartite, circular, single-stranded (css) DNA genomes. Each of the six (genus Babuvirus) or eight (genus Nanovirus) genomic DNAs is 0.9–1.1 kb and is separately encapsidated. Many isolates are associated with satellite-like cssDNAs (alphasatellites) of 1.0–1.1 kb. Hosts are eudicots, predominantly legumes (genus Nanovirus), and monocotyledons, predominantly in the order Zingiberales (genus Babuvirus). Nanovirids require a virus-encoded helper factor for transmission by aphids in a circulative, non-propagative manner. This is a summary of the ICTV Report on the family Nanoviridae, which is available at ictv.global/report/nanoviridae.
Published: 2021
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14. Climate change impact and adaptation for wheat protein

Author: Mohamed Jabloun, Pierre Martre, Garry O'Leary, Dominique Ripoche, Bruno Basso, Pramod K. Aggarwal, Daniel Wallach, Matthew P. Reynolds, Marijn van der Velde, John R. Porter, Heidi Webber, Enli Wang, Frank Ewert, Joost Wolf, Christian Klein, Belay T. Kassie, Christian Biernath, Margarita Garcia-Vila, M. Ali Babar, Pierre Stratonovitch, Yujing Gao, Glenn J. Fitzgerald, Davide Cammarano, Bing Liu, Peter J. Thorburn, Fulu Tao, Andrew J. Challinor, Reimund P. Rötter, Christine Girousse, Zhigan Zhao, Christoph Müller, Ann-Kristin Koehler, Jørgen E. Olesen, Elias Fereres, Iwan Supit, Andrea Maiorano, Marco Bindi, Sebastian Gayler, Kurt Christian Kersebaum, Giacomo De Sanctis, Alex C. Ruane, Rosella Motzo, Juraj Balkovic, Manuel Montesino San Martin, Roberto Ferrise, Mikhail A. Semenov, Claudio O. Stöckle, Soora Naresh Kumar, Gerrit Hoogenboom, Benjamin Dumont, Ehsan Eyshi Rezaei, Mukhtar Ahmed, Senthold Asseng, Thilo Streck, Yan Zhu, R. Cesar Izaurralde, Katharina Waha, Ahmed M. S. Kheir, Taru Palosuo, Liujun Xiao, Sara Minoli, Eckart Priesack, Heidi Horan, Curtis D. Jones, Francesco Giunta, Zhao Zhang, Claas Nendel, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), European Food Safety Authority = Autorité européenne de sécurité des aliments, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Department of Economic Drt and Resources, Grains Innovation Park, Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Faculty of Veterinary and Agricultural Science [Melbourne], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Department of Agricultural Sciences, University of Naples Federico II = Università degli studi di Napoli Federico II, World Food Crops Breeding, Department of Agronomy, IFAS, University of Florida [Gainesville] (UF), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Soils, Water and Environment Research Institute, Agricultural Research Center (ARC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, University of El-Tarf, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Earth and Environmental Sciences [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Institute of Biochemical Plant Pathology, Research Center for Environmental Health, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Agri‐food Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence (UniFI), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and Food Security (CCAFS), International Center for Tropical Agriculture, GMO Unit, European Food Safety Authority, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Crop Sciences, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Biochemical Plant Pathology [Neuherberg], German Research Center for Environmental Health - Helmholtz Center München (GmbH), National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Member of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Centre for Environment Science and Climate Resilient Agriculture [New Delhi], Indian Agricultural Research Institute (IARI), Natural Resources Institute Finland (LUKE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), University of Lincoln, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Water & Food and Water Systems & Global Change Group, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Joint Research Centre (IPTS), Commission Européenne, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), CSIRO Agriculture and Food (CSIRO), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), National Research Foundation for the Doctoral Program of Higher Education of China, Grant/Award Number: 20120097110042, International Food Policy, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Georg-August-University [Göttingen], Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, University of Naples Federico II, Helmholtz-Zentrum München (HZM), Universtiy of Florence, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Natural Resources Institute Finland, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Wageningen University, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Georg-August-Universität Göttingen, University of Goettingen, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Research Program on Climate Change, Agriculture and Food Security, BISA‐CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Comenius University [Bratislava], Helmholtz Zentrum München, Institute of Crop Science and Resource Conservation INRES, University of Bonn, IAS‐CSIC, Universidad de Cordoba, Institute for Sustainable Food Systems, Texas A&M AgriLife Research and Extension Center, Aarhus University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Wageningen University and Research Center (WUR), Beijing Normal University, and China Agricultural University
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Water en Voedsel, 01 natural sciences, grain protein, adaptation au milieu, climate change adaptation, climate change impact, food security, wheat, Co2 concentration, adaptation to the environment, Triticum, General Environmental Science, 2. Zero hunger, changement climatique, Global and Planetary Change, Food security, Ecology, Temperature, food and beverages, Adaptation, Physiological, Droughts, Nitrogen, Climate Change, Climate change, 010603 evolutionary biology, blé, Crop production, Food Quality, Grain quality, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, Grain Proteins, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, Global change, Carbon Dioxide, Models, Theoretical, 15. Life on land, Agronomy, 13. Climate action, Grain yield, Environmental science, Water Systems and Global Change, Protein concentration
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).
Published: 2019
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15. Ensemble modelling of carbon fluxes in grasslands and croplands

Author: Elizabeth Pattey, Ute Skiba, Brian Grant, Gianni Bellocchi, Katja Klumpp, Pete Smith, Matthew T. Harrison, Val Snow, Sylvie Recous, Joanna Sharp, Fiona Ehrhardt, Arti Bhatia, Joël Léonard, Raphaël Martin, Andrew D. Moore, Jordi Doltra, Renata Sándor, Lorenzo Brilli, Jean-François Soussana, Peter Grace, Susanne Rolinski, Ward Smith, Vasileios Myrgiotis, Raia Silvia Massad, Qing Zhang, Miko U. F. Kirschbaum, Lianhai Wu, Nuala Fitton, Luca Doro, Bruno Basso, Christopher D. Dorich, Patricia Laville, Producció Vegetal, Cultius Extensius Sostenibles, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Direction scientifique Environnement, Forêt et Agriculture, Institut National de la Recherche Agronomique (INRA), Queensland University of Technology, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Aberdeen, AgResearch Grasslands, Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Università degli Studi di Sassari, Agriculture and Agri-Food [Ottawa] (AAFC), British Geological Survey (BGS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro (Transfrontalière BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Computer Laboratory [Cambridge], University of Cambridge [UK] (CAM), Scotland's Rural College (SRUC), Potsdam Institute for Climate Impact Research (PIK), Plant & Food Research, Natural Environment Research Council (NERC), Rothamsted Research, CN-MIP by Joint Programming Initiative 'FACCE' Models4Pastures by Joint Programming Initiative 'FACCE' MACSUR by Joint Programming Initiative 'FACCE' COMET-Global by Joint Programming Initiative 'FACCE' MAGGNET by Joint Programming Initiative 'FACCE' French Embassy in Budapest (Hungary) by way of 'Make Our Planet Great Again' programme Federal Ministry of Education & Research (BMBF)01LN1317AEuropean Union (EU)EC EVK2-CT2001-00105GOCE-CT-2003-505572017841SRUC, Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), and Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: 0106 biological sciences, [SDE.MCG]Environmental Sciences/Global Changes, Soil Science, Growing season, Atmospheric sciences, 01 natural sciences, Grassland, Temperate climate, 2. Zero hunger, Biomass (ecology), geography, geography.geographical_feature_category, Primary production, 04 agricultural and veterinary sciences, Soil carbon, 15. Life on land, Crop rotation, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Ecosystem respiration, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). info:eu-repo/semantics/acceptedVersion
Published: 2020
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16. Tuta absoluta continues to disperse in Asia: damage, ongoing management and future challenges

Author: Su Wang, Nicolas Desneux, Uulu Tair Esenali, Abid Ali, Peng Han, Fang-Hao Wan, Fariba Sohrabi, Antonio Biondi, Anvar Jalilov, P. R. Shashank, Guifen Zhang, Yunus Bayram, Liora Shaltiel-Harpaz, Anitha Saji, Khasan Ismoilov, Zhaozhi Lu, Chinese Academy of Sciences [Beijing] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), General Directorate Of Food And Control, Galilee Research Institute (Migal), Persian Gulf University, Kyrgyz-Turkish Manas University (KTU MANAS), Academy of Sciences of the Republic of Tajikistan, University of Agriculture Faisalabad (UAF), Indian Agricultural Research Institute (IARI), Institute of Plant and Environment Protection, Beijing Academy of Agriculture and Forestry Sciences, Chinese Academy of Agricultural Sciences (CAAS), Department of Agriculture, Food and Environment, Université de Catane, Institut Sophia Agrobiotech (ISA), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA)
Subjects: 0106 biological sciences, Integrated pest management, Invasive pest, [SDV]Life Sciences [q-bio], Biology, 010603 evolutionary biology, 01 natural sciences, Cross-regional network, RNA interference, Genetically modified crops, Asian country, Economic impact analysis, China, 2. Zero hunger, business.industry, Agroforestry, fungi, food and beverages, 15. Life on land, biology.organism_classification, Biological control, Chemical control, Quarantine, Sterile insect technique, Agronomy and Crop Science, 010602 entomology, Agriculture, South american, [SDE]Environmental Sciences, Tuta absoluta, PEST analysis, business
Abstract: Since its initial detection in Turkey in 2009, the invasive destructive pest South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) has started its new invasion journey to east and invaded most Asian countries. This pest currently causes extensive damage to tomato production and potentially threatens key production areas such as China. To provide an overview of current status of T. absoluta in Asia, we have briefly reviewed the damage and economic impacts by this pest locally and discussed why this species has spread so rapidly among the countries. Moreover, ongoing integrated pest management options are summarized in newly invaded areas with an emphasis of discussing the potential control failures by chemical insecticides. Future research efforts on developing promising management technologies are recommended. Finally, we suggest building a cross-regional network to enhance the sustainable control of this pest.
Published: 2019
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17. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author: Mónica Espadafor, Bing Liu, Frank Ewert, Mukhtar Ahmed, Liujun Xiao, Thilo Streck, Senthold Asseng, Soora Naresh Kumar, Gerrit Hoogenboom, John R. Porter, Sara Minoli, Margarita Garcia-Vila, Joost Wolf, Juraj Balkovic, Alex C. Ruane, Giacomo De Sanctis, Pierre Martre, Roberto C. Izaurralde, Marijn van der Velde, Dominique Ripoche, Roberto Ferrise, Davide Cammarano, Fulu Tao, Bruno Basso, Christoph Müller, Heidi Webber, Yujing Gao, Andrea Maiorano, Christian Klein, Ann-Kristin Koehler, Andrew J. Challinor, Reimund P. Rötter, Garry O'Leary, Manuel Montesino San Martin, Eckart Priesack, Peter J. Thorburn, Heidi Horan, Kurt Christian Kersebaum, Iwan Supit, Zhigan Zhao, Taru Palosuo, Belay T. Kassie, Christian Biernath, Pramod K. Aggarwal, Katharina Waha, Sebastian Gayler, Daniel Wallach, Yan Zhu, Marco Bindi, Zhao Zhang, Claas Nendel, Enli Wang, Curtis D. Jones, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Copenhagen = Københavns Universitet (KU), Lincoln University, University of Leeds, CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, Potsdam Institute for Climate Impact Research (PIK), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Comenius University in Bratislava, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Agrifood Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), The James Hutton Institute, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Université de Liège, University of Córdoba, Department of Crop Sciences, Georg-August-University [Göttingen], Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, European Food Safety Authority (EFSA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Agriculture Victoria Research, Institute for Natural Resources, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, Rothamsted Research, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), European Commission - Joint Research Centre [Ispra] (JRC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP), and Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/, P016855/1
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Food prices, Water en Voedsel, Climate change, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Model ensemble, Extreme low yields, model ensemble, 1.5°C warming, Temperate climate, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, WIMEK, Water and Food, Food security, Ecology, business.industry, wheat production, Crop yield, Global warming, food security, 15. Life on land, PE&RC, climate change, Plant Production Systems, 13. Climate action, Agriculture, Plantaardige Productiesystemen, extreme low yields, 8. Economic growth, Environmental science, Wheat production, Water Systems and Global Change, business, Cropping
Abstract: Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.
Published: 2019
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18. Quantification of modelling uncertainties in an ensemble of carbon simulations in grasslands and croplands

Author: Sandor, Renata, Bellocchi, Gianni, Ehrhardt, Fiona, Bhatia, A., Brilli, Lorenzo, de Antoni Migliorati, Massimiliano, Carozzi, Marco, Doltra, Jordi, Dorich, Chris, Doro, Luca, Fitton, Nuala, Fuchs, K, Gongadze, Kate, Grace, Pete, Grant, B., Giacomini, S.J., Klumpp, Katja, Léonard, L, Liebig, M., Martin, Raphaël, Massad, Raia Silvia, Merbold, Lutz, Newton, P., Pattey, Elizabeth, Rees, B., Rolinski, Susanne, Sharp, Johanna, Smith, P., Smith, W., Snow, Val, Soussana, Jean-François, Zhang, Q, Recous, Sylvie, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Agricultural Research Institute, Direction scientifique Environnement, Forêt et Agriculture, Institut National de la Recherche Agronomique (INRA), Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Queensland University of Technology, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Catabrian Agricultural Research and Training Center (CIFA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Università degli Studi di Sassari, Collège de Direction (CODIR), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), and CN-MIP
Subjects: flux de carbon, flux de carbone, modélisation, grandes cultures, prairies, ensemble, [SDV]Life Sciences [q-bio], prairie, [SDE]Environmental Sciences, [SDV.IDA]Life Sciences [q-bio]/Food engineering, grande culture, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019

19. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Author: Fiona Ehrhardt, Mark A. Liebig, Raphaël Martin, Jordi Doltra, Russel McAuliffe, Val Snow, Joël Léonard, Andrew D. Moore, Stephanie K. Jones, Lutz Merbold, Pete Smith, Lianhai Wu, Elizabeth A. Meier, Paul C. D. Newton, Arti Bhatia, Gianni Bellocchi, Massimiliano De Antoni Migliorati, Miko U. F. Kirschbaum, Ward Smith, Brian Grant, Renáta Sándor, Joanna Sharp, Lorenzo Brilli, Nuala Fitton, Jean-François Soussana, Elizabeth Pattey, Luca Doro, Katja Klumpp, Christopher D. Dorich, Bruno Basso, Raia Silvia Massad, Sylvie Recous, Patricia Laville, Qing Zhang, Matthew T. Harrison, Sandro José Giacomini, Susanne Rolinski, Mark Lieffering, Peter Grace, Vasileios Myrgiotis, Collège de Direction (CODIR), Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Queensland University of Technology, Agresearch Ltd, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), University of Aberdeen, Michigan State University [East Lansing], Michigan State University System, Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Colorado State University [Fort Collins] (CSU), Università degli Studi di Sassari, Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Potsdam Institute for Climate Impact Research (PIK), New Zealand Institute for Crop and Food Research, Partenaires INRAE, Rothamsted Research, Chinese Academy of Sciences (CAS), ANR, European Project: 277610,EC:FP7:KBBE,FP7-JPROG-2011-RTD,FACCE CSA(2011), Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), Biotechnology and Biological Sciences Research Council (BBSRC), and Universidade Federal de Santa Maria (UFSM)
Subjects: biogeochemical models, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, Atmospheric sciences, 01 natural sciences, Grassland, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Environmental Chemistry, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Agricultural productivity, 0105 earth and related environmental sciences, General Environmental Science, agriculture, 2. Zero hunger, Global and Planetary Change, geography, geography.geographical_feature_category, Ecology, Ensemble forecasting, nitrous oxide, Crop yield, Simulation modeling, 04 agricultural and veterinary sciences, benchmarking, climate change, greenhouse gases, soil, yield, Climate change mitigation, Agronomy, 13. Climate action, greenhouse gas, Greenhouse gas, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: International audience; Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2O)emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents.Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grass-land growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields(from 36% at stage 1 down to 4% on average) and grassland productivity (from 44%to 27%) and to a lesser and more variable extent for N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.
Published: 2018
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20. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

Author: Roberto C. Izaurralde, Jakarat Anothai, Andrew J. Challinor, Reimund P. Rötter, Jørgen E. Olesen, Curtis D. Jones, Bing Liu, T. Palosuo, Peter J. Thorburn, Kurt Christian Kersebaum, Mohamed Jabloun, Iwan Supit, Frank Ewert, Mikhail A. Semenov, Margarita Garcia-Vila, Claudio O. Stöckle, Benjamin Dumont, Belay T. Kassie, Jordi Doltra, Christian Biernath, Dominique Ripoche, Giacomo De Sanctis, Enli Wang, Elias Fereres, Bruce A. Kimball, Thilo Streck, Gerard W. Wall, L. A. Hunt, Pierre Stratonovitch, Fulu Tao, Jeffrey W. White, Christoph Müller, Bruno Basso, Senthold Asseng, Katharina Waha, Ann-Kristin Koehler, Andrea Maiorano, Ehsan Eyshi Rezaei, Eckart Priesack, Soora Naresh Kumar, Claas Nendel, Gerrit Hoogenboom, Davide Cammarano, David B. Lobell, Joost Wolf, Pierre Martre, Pramod K. Aggarwal, Garry O'Leary, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Yan Zhu, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Consultative Group on International Agricultural Research (CGIAR), AgWeatherNet Program, Washington State University (WSU), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Landscape & Water Sciences, Department of Environment of Victoria, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, PPS and WSG &CALM, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), College of Agronomy and Biotechnology, and Southwest University
Subjects: Simulations, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Water en Voedsel, Wheat, Field experimental data, Heat stress, Crop model simulations, AgMIP, Hot Serial Cereal, 01 natural sciences, donnée expérimentale, Crop, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Cultivar, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Water and Food, Vegetal Biology, Global warming, Sowing, essai en plein champ, food and beverages, série climatique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Agronomy, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, Modeling and Simulation, Frost, Weather data, Environmental science, Water Systems and Global Change, stress hydrique, Biologie végétale, modèle de production, 010606 plant biology & botany
Abstract: The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Published: 2018
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21. Multimodel ensembles improve predictions of crop–environment–management interactions

Author: Giacomo De Sanctis, Taru Palosuo, Davide Cammarano, Frank Ewert, Soora Naresh Kumar, Roberto C. Izaurralde, Gerrit Hoogenboom, Elias Fereres, Bing Liu, Thilo Streck, Mikhail A. Semenov, Ehsan Eyshi Rezaei, Peter J. Thorburn, Claudio O. Stöckle, Benjamin Dumont, Andrea Maiorano, Eckart Priesack, Iwan Supit, Heidi Horan, Kurt Christian Kersebaum, Pierre Martre, Margarita Garcia-Vila, Dominique Ripoche, Pierre Stratonovitch, Yujing Gao, Zhao Zhang, Ann-Kristin Koehler, Curtis D Jones, Fulu Tao, Claas Nendel, Christoph Müller, Andrew J. Challinor, Reimund P. Rötter, Mukhtar Ahmed, Senthold Asseng, Christine Girousse, Bruno Basso, Christian Klein, Pramod K. Aggarwal, Joost Wolf, Glenn J. Fitzgerald, Martin K. van Ittersum, Garry O'Leary, Belay T. Kassie, Christian Biernath, Sebastian Gayler, Daniel Wallach, Sara Minoli, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Nanjing Agricultural University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Systems Group, Wageningen University and Research [Wageningen] (WUR), Agriculture and Food Security (CCAFS), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Plant Pathology, The James Hutton Institute, University of Leeds, Consultative Group on International Agricultural Research (CGIAR), GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Center for Development Research, University of Córdoba, Agriculture Victoria Research, University of Melbourne, Institute of Soil Science and Land Evaluation, University of Hohenheim, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), University of Florida [Gainesville] (UF), Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Institute of landscape systems analysis, School of Earth and Environment, European Food Safety Authority (EFSA), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), University Medical Center Göttingen (UMG), Computational and Systems Biology Department, Rothamsted Research, Water & Food and Water Systems & Global Change Group, Wageningen University, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University (BNU), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Université de Toulouse (UT)-Université de Toulouse (UT), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Nanjing Agricultural University (NAU), Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), and Chinese Academy of Sciences [Beijing] (CAS)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, Mean squared error, Climate Change, [SDE.MCG]Environmental Sciences/Global Changes, Inverse, Water en Voedsel, Monotonic function, multi-model ensemble, Environment, High skill, 01 natural sciences, crop models, ensemble mean, Statistics, Environmental Chemistry, Triticum, 0105 earth and related environmental sciences, General Environmental Science, Mathematics, 2. Zero hunger, ensemble median, Global and Planetary Change, WIMEK, Water and Food, Ecology, Ensemble average, Simulation modeling, Agriculture, 04 agricultural and veterinary sciences, Variance (accounting), prediction, Models, Theoretical, PE&RC, Plant Production Systems, multimodel ensemble, 13. Climate action, Plantaardige Productiesystemen, climate change impact, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water Systems and Global Change, Environment management
Abstract: International audience; A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Published: 2018
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22. C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Author: Pete Smith, Sandro José Giacomini, Gianni Bellocchi, Joël Léonard, M. de Antoni Migliorati, Renata Sándor, Chris Dorich, Arti Bhatia, Ward Smith, Paul C. D. Newton, Jordi Doltra, Raia Silvia Massad, Fiona Ehrhardt, Miko U. F. Kirschbaum, Elizabeth Pattey, Matthew T. Harrison, Qing Zhang, Andrew D. Moore, Russell McAuliffe, Mark A. Liebig, Susanne Rolinski, Raphaël Martin, Brian Grant, Vasileios Myrgiotis, Mark Lieffering, Lorenzo Brilli, Jean-François Soussana, Luca Doro, Katja Klumpp, Bruno Basso, Elizabeth A. Meier, Patricia Laville, Sylvie Recous, Val Snow, Peter Grace, Lutz Merbold, Joanna Sharp, Nuala Fitton, Stephanie K. Jones, Lianhai Wu, Institute for Soil Sciences and Agricultural Chemistry (ATK TAKI), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA)-Hungarian Academy of Sciences (MTA), UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Institut National de la Recherche Agronomique (INRA), Collège de Direction (CODIR), Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Indian Agricultural Research Institute (IARI), DISPAA, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Queensland University of Technology, Cantabrian Agricultural Research and Training Centre, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Desertification Research Group, University of Sassari, Institute of Biological and Environmental Sciences, University of Aberdeen, Soil Department, Federal University of Santa Maria, Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), United States Department of Agriculture, Lincoln Research Centre, AgResearch Ltd, Grasslands Research Centre, Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mazingira Centre, International Livestock Research Institute, Black Mountain Laboratories, CSIRO Agriculture Flagship, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Potsdam Institute for Climate Impact Research (PIK), Plant & Food Research, Sustainable Soils and Grassland Systems, Rothamsted Research, Institute of Atmospheric Physics [Beijing] (IAP), Chinese Academy of Sciences [Beijing] (CAS), ANR, European Project: 277610, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Collège de Direction, University of Florence (UNIFI), Scotland's Rural College (SCUR), Landcare Research, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), New Zealand Institute for Plant and Food Research Limited, Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Unité de recherche sur l'Ecosystème Prairial (UREP), Università degli Studi di Firenze = University of Florence (UniFI), Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: 010504 meteorology & atmospheric sciences, Calibration (statistics), [SDV]Life Sciences [q-bio], ANPP, Context (language use), Atmospheric sciences, 01 natural sciences, Grassland, Physics::Geophysics, Benchmark (surveying), C-N MIP, Quantitative Biology::Populations and Evolution, model intercomparaison, 0105 earth and related environmental sciences, 2. Zero hunger, geography, geography.geographical_feature_category, Ensemble forecasting, Ecology, Simulation modeling, 04 agricultural and veterinary sciences, General Medicine, 15. Life on land, grassland production, 13. Climate action, Greenhouse gas, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Arable land
Abstract: Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.
Published: 2016
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23. Indole acetic acid and ACC deaminase-producing Rhizobium leguminosarum bv. trifolii SN10 promote rice growth, and in the process undergo colonization and chemotaxis

Author: Clémence Chaintreuil, Philippe Jourand, Rumpa Biswas Bhattacharjee, Aqbal Singh, Satya Narayan Mukhopadhyay, Bernard Dreyfus, University of Guelph, Indian Institute of Technology Delhi (IIT Delhi), Laboratoire des symbioses tropicales et méditerranéennes (UMR LSTM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Indian Agricultural Research Institute (IARI), Institut National de la Recherche Agronomique (INRA), Council of Scientific and Industrial Research, India, and 'Sandwich Program' Fellowship
Subjects: 0106 biological sciences, Root nodule, [SDV]Life Sciences [q-bio], Biofertilizer, WHEAT, Soil Science, Biology, Umbelliferone, Rhizobacteria, medicine.disease_cause, 01 natural sciences, Microbiology, Rhizobium leguminosarum, chemistry.chemical_compound, Botany, medicine, ROOT-SYSTEM, Flavonoids, 2. Zero hunger, Plant growth promoting rhizobacteria (PGPR), Inoculation, Chemotaxis, food and beverages, 04 agricultural and veterinary sciences, INOCULATION, biology.organism_classification, AZOSPIRILLUM-BRASILENSE, SOILS, PSEUDOMONAS-PUTIDA, chemistry, PLANT-GROWTH, ESCHERICHIA-COLI, BACTERIA, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Rice, Agronomy and Crop Science, Bacteria, RHIZOBACTERIA, Rhizobium, 010606 plant biology & botany
Abstract: International audience; This study focuses on the chemotaxis, colonization and rice growth promoting ability of indole acetic acid (IAA) and 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase-producing rhizobacteria Rhizobium leguminosarum bv. trifolii SN10, previously isolated from root nodules of Trifolium alexandrium L. We show here that R. leguminosarum bv. trifolii SN10 promote the growth of four different rice varieties grown in India in terms of biomass, root branching and N content. In addition, using scanning electron microscopy and viable cell counts, we provide evidence that the bacteria successfully colonize the root surface of the rice variety which showed maximum growth promotion upon inoculation. Not only this, R. leguminosarum bv. trifolii SN10 exhibit a strong chemotaxis response towards the rice seed and root exudates despite the presence of a bacteriostatic phenolic compound, 7-hydroxycoumarin (umbelliferone). Further, R. leguminosarum bv. trifolii SN10 secretion of phytohormones such as IAA and ACC deaminase suggest the potential of the plant growth promoting rhizobacteria to be used as biofertilizer to enhance rice crop production in the subcontinent.
Published: 2011
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24. Inter-comparison of wheat models to identify knowledge gaps and improve process modeling

Author: Wang, E., Martre, Pierre, Asseng, S., Ewert, F., Zhao, Z., Maiorano, Andrea, Rotter, R. P., Kimball, B. A., Ottman, Michael J., Wall, G. W., White, J. W., Aggarwal, P. K., Alderman, P. D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, Giacomo, Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K.C., Koehler, A. K., Müller, C., Liu, L., Kumar Naresh, S., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Ripoche, Dominique, Ruane, A. C., Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, Daniel, Wolf, J., Zhu, Y., Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland (LUKE), ARS/ALARC, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), AgWeatherNet Program, Washington State University (WSU), Michigan State University [East Lansing], Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, International Center for Tropical Agriculture, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], IAS, Princeton University, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Nanjing Agricultural University, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape & Water Sciences, Department of Environment of Victoria, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Institute of Soil Science and Land Evaluation, University of Hohenheim, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: blé, Modeling and Simulation, comparaison de modèles, température, modèle phénologique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS, modèle de production, incertitude
Abstract: International audience
Published: 2016

25. How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use?

Author: Durand, Jean-Louis, Delusca, Kénel, Boote, K., Lizaso, J., Manderscheid, R., Rosenzweig, C., Jones, J., Weigel, H.J., Ruane, A., Anapalli, S., Ahuja, L., Basso, B., Baron, C., Bertuzzi, Patrick, Ripoche, Dominique, Biernath, C., Priesak, E., Derynge, D., Ewert, F., Gaiser, T., Gayler, S., Heilein, F., Kersebaum, K.C., Kim, S.H., Müller, C., Nendel, C., Ramirez, J., Tao, F., Timlin, D., Waha, K., Twine, T., Wang, E., Webber, H., Zhao, Z., Rötter, R., Srivastava, A., Seidel, S., Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), ETSIA, Johann Heinrich von Thünen Institut, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Colorado State University [Fort Collins] (CSU), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Michigan State University System, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Agroclim (AGROCLIM), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), School of Earth and Environment, University of Leeds, Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), United States Department of Agriculture (USDA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Soil, Water and Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Rheinische Friedrich-Wilhelms-Universität Bonn, China Agricultural University (CAU), Natural Resources Institute Finland (LUKE), Institute of Crop Science and Resource Conservation [Bonn], Technische Universität Dresden = Dresden University of Technology (TU Dresden), and ProdInra, Migration
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2015

26. Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation

Author: Li, Tao, Yin, Xinyou, Hasegawa, Toshihiro, Boote, Ken, Zhu, Yan, Myriam ADAM, Baker, Jeff, Bouman, Bas, Bregaglio, Simone, Buis, Samuel, Confalonieri, Roberto, Fugice, Job, Fumoto, Tamon, Gaydon, Donald, Kumar, S. N., Lafarge, Tanguy, Marcaida, Manuel, Masutomi, Y., Nakagawa, Hitochi, Pequeno, D. N. L., Ruane, Alex C., Ruget, Françoise, Singh, Upendra, Tang, Liang, Tao, Fulu, Wallach, Daniel, Wilson, Lloyd Ted, Yang, Yubin, Yoshida, Hiroe, Zhang, Zhao, Zhu, Jinyu, International Rice Research Institute, Centre for Crop Systems Analysis, Wageningen University and Research Centre [Wageningen] (WUR), National Institute of Agro-Environmental Sciences (NIAES), University of Florida [Gainesville], National Engineering and Technology Center for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), WCA-Resilient Dryland Systems, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Agricultural Research Service, United States Department of Agriculture, Cassandra lab, University of Milan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Muscle Shoals, International Fertilizer Development Center (IFDC), CSIRO, Indian Agricultural Research Institute (IARI), College of Agriculture, Northeast Agricultural University [Harbin], National Agriculture and Food Research Organization, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Texas A&M University System, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Sciences, Chinese Academy of Sciences [Changchun Branch] (CAS), International Rice Research Institute [Philippines] (IRRI), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Wageningen University and Research [Wageningen] (WUR), University of Florida [Gainesville] (UF), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), International Crops Research Institute for the Semi-Arid Tropics [Inde] (ICRISAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), USDA-ARS : Agricultural Research Service, National Agriculture and Food Research Organization (NARO), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Beijing Normal University (BNU), ProdInra, Archive Ouverte, Wageningen University and Research Centre [Wageningen] ( WUR ), National Institute for Agro-Environmental Sciences, University of Florida, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), International Crops Research Institute for the Semi-Arid Tropics ( ICRISAT ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Institut National de la Recherche Agronomique ( INRA ), International Fertilizer Development Center ( IFDC ), Indian Agricultural Research Institute ( IARI ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Chinese Academy of Sciences [Beijing] ( CAS ), Texas A and M AgriLIFE Research Center at Beaumont, and Chinese Academy of Sciences [Changchun Branch] ( CAS )
Subjects: production rizicole, [ SDV ] Life Sciences [q-bio], U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, F60 - Physiologie et biochimie végétale, crop model, rice, flux de co2, [SDV]Life Sciences [q-bio], education, food and beverages, F62 - Physiologie végétale - Croissance et développement, humanities, fertilisation, [SDV] Life Sciences [q-bio], high temperature, croissance des graines, calibrage du modèle, F01 - Culture des plantes, fertilization, haute température, mesure par chambre, biomasse aérienne, reproductive and urinary physiology, health care economics and organizations
Abstract: Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevation . International Crop Modelling Symposium

27. How precisely do maize crop models simulate the impact of climate change variables on yields and water use?

Author: Durand, J. L., Bassu, Simona, Brisson, Nadine, Boote, Kenneth J., Lizano, Jon, Jones, James W., Rosenzweig, Cynthia, Ruane, Alex C., Myriam ADAM, Baron, Christian, Basso, Bruno, Biernath, Christian, Boogaard, Hendrik, Conijn, Sjaak, Corbeels, Marc, Deryng, Delphine, Sanctis, Giacomo, Gayler, Sebastian, Grassini, Patricio, Hatfield, Jerry L., Hoek, Steven B., Izaurralde, Cesar, Jongschaap, Raymond, Kemanian, Armen, Kersebaum, Kurt Christian, Kim, Soo-Hyung, Kumar, Naresh S., Makowski, David, Müller, Christoph, Nendel, Claas, Priesack, Eckart, Pravia, Maria Virginia, Sau, Federico, Shcherbak, Iurii, Tao, Fulu, Teixeira, Edmar, Timlin, Dennis, Waha, Katharina, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Department of agronomy, University of Florida [Gainesville] (UF), Department Produccion vegetal, Fitotecnia, Universidad Politécnica de Madrid (UPM), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), National Aeronautics and Space Administration, Partenaires INRAE, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department crop systems, forestry and environmental sciences, University of Basilicata, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Centre for Geo-Information, ALTERRA, Plant Research International (PRI), Wageningen University and Research [Wageningen] (WUR), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Tyndall Centre for Climate Change Research, University of East Anglia [Norwich] (UEA), Agroclim (AGROCLIM), Water and earth system science [Tübingen] (WESS), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of agronomy and horticulture, University of Nebraska [Lincoln], University of Nebraska System-University of Nebraska System, United States Department of Agriculture (USDA), Pacific Northwest National Laboratory (PNNL), University of Maryland [College Park], University of Maryland System, Department of Plant Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), University of Washington [Seattle], Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Potsdam Institute for Climate Impact Research (PIK), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Sustainable Production, Plant & Food Research, ARS Crop Systems and Global Change Laboratory, United States Department of Agriculture, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and ProdInra, Migration
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, F01 - Culture des plantes, P40 - Météorologie et climatologie, U10 - Informatique, mathématiques et statistiques, F62 - Physiologie végétale - Croissance et développement, P10 - Ressources en eau et leur gestion, ComputingMilieux_MISCELLANEOUS
Abstract: AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, i.e. good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way to approach the impact of climate change on maize yields. There was also a broad agreement between models to simulate a reduction in maize yield in response to temperature, roughly - 0.5 Mg ha-1 per °C increase, with no significant impact on water use, although the latter variable was estimated with a large variability between models. Plant phenology was the mostly altered process with increasing temperature. Shortening of the duration from flowering to maturity in particular reduced the gain in grain weight during that phase. This suggests that genetics could hence play a key role in adapting maize production to climate change, at least under high water availability. Doubling [CO2] from 360 to 720 μmole mole-1 increased grain yield by 7.5% on average across models and sites, with a slight decrease of water use, bringing about an increase in water use efficiency. However, the variability of the response to [CO2] was very high, bringing about the need to better simulate the role of CO2, especially on plant transpiration. In a second phase, models are therefore now being tested against Free Air CO2 Enrichment experimental data, so that variability can be reduced and the actual impact of global change on water use can be assessed with a relevant precision to adaptating agricultural practices. (Texte intégral)
Published: 2015

28. Molecular cloning and in-silico characterization of high temperature stress responsive pAPX gene isolated from heat tolerant Indian wheat cv. Raj 3765

Author: Rahul Singh Jasrotia, Koushik Biswas, Jasdeep Chatrath Padaria, Harinder Vishwakarma, G. P. Singh, Biotechnology and Climate Change Laboratory, National Research Centre on Plant Biotechnology, Division of Genetics, Indian Agricultural Research Institute ( IARI ), and Dupuis, Christine
Subjects: Models, Molecular, Proteomics, Hot Temperature, Protein Conformation, Molecular Sequence Data, Expression, [SDV.GEN] Life Sciences [q-bio]/Genetics, Molecular cloning, Biology, General Biochemistry, Genetics and Molecular Biology, Homology (biology), Structure-Activity Relationship, Ascorbate Peroxidases, Peptide mass fingerprinting, Gene Expression Regulation, Plant, Complementary DNA, cv. Raj3765, Peroxisomal ascorbate peroxidase, Amino Acid Sequence, Heat shock, Cloning, Molecular, Gene, Phylogeny, Triticum, Plant Proteins, Medicine(all), Expression vector, Biochemistry, Genetics and Molecular Biology(all), cDNA library, [ SDV.BIO ] Life Sciences [q-bio]/Biotechnology, food and beverages, Reproducibility of Results, Homology modeling, General Medicine, Adaptation, Physiological, [SDV.BIO] Life Sciences [q-bio]/Biotechnology, Molecular Docking Simulation, Oxidative Stress, Biochemistry, In-silico, Lipid Peroxidation, [ SDV.GEN ] Life Sciences [q-bio]/Genetics, Heat-Shock Response, cv Raj3765, Research Article, Cloning
Abstract: Background Heat stress leads to accelerated production of reactive oxygen species (ROS) which causes a huge amount of oxidative damage to the cellular components of plants. A large number of heat stress related genes as HSPs, catalases, peroxidases are overexpressed at the time of stress. A potent stress responsive gene peroxisomal ascorbate peroxidase (TapAPX) obtained from heat stress (42°C) responsive subtractive cDNA library from a thermo tolerant wheat cv. Raj3765 at anthesis stage was cloned, characterized and its role was validated under heat stress by proteomics and in-silico studies. In the present study we report the characterization at molecular and in-silico level of peroxisomal TapAPX gene isolated from heat tolerant wheat cultivar of India. Results qPCR studies of TapAPX gene displayed up to 203 fold level of expression at 42°C heat stress exposure. A full length cDNA of 876 bp obtained by RACE deduced a protein of 292 amino acid residues which gives a complete 3D structure of pAPX by homology modeling. TapAPX cDNA was cloned in expression vector pET28 (a+) and the recombinant protein over-expressed in E. coli BL21 showed highest homology with APX protein as deduced by peptide mass fingerprinting. Conclusions TapAPX gene from wheat cv Raj3765 has a distinct role in conferring thermo tolerance to the plants and thus can be used in crop improvement programmes for development of crops tolerant to high temperature. Electronic supplementary material The online version of this article (doi:10.1186/1756-0500-7-713) contains supplementary material, which is available to authorized users.
Published: 2014

29. The genome of flax ( Linum usitatissimum ) assembled de novo from short shotgun sequence reads

Author: Quentin C. B. Cronk, Neil Hobson, Jun Wang, Georgina M. Lambert, Polumetla Ananda Kumar, Shilin Zhu, Simon Hawkins, Sylvie Cloutier, Zhiwen Wang, Linfeng Yang, Daihu Shi, Leonardo Galindo, Godfrey Neutelings, David W. Galbraith, Raju Datla, Christopher J. Grassa, Joshua McDill, Gane Ka-Shu Wong, Michael K. Deyholos, Christopher A. Cullis, Armando Geraldes, Andrew G. Sharpe, Prasanta K. Dash, Bei Shan Ind Zone, Partenaires INRAE, University of Alberta, Stress Abiotiques et Différenciation des Végétaux Cultivés (SADV), Institut National de la Recherche Agronomique (INRA)-Université de Lille, Sciences et Technologies, National Research Council of Canada (NRC), Sch Plant Sci, University of Arizona, BIO5 Institute, Dept Bot, University of British Columbia (UBC), Case Western Reserve University [Cleveland], Indian Agricultural Research Institute (IARI), Genome Alberta/Genome Canada, Government of Alberta, Alberta Innovates Technology Futures-iCORE, Institut National de la Recherche Agronomique France, and Indian Council of Agricultural Research
Subjects: 0106 biological sciences, Chromosomes, Artificial, Bacterial, plant genome, Arabidopsis thaliana, [SDV]Life Sciences [q-bio], Malpighiales, expressed sequence tag, Sequence assembly, DNA sequences, Plant Science, 01 natural sciences, Genome, Contig Mapping, CODING DNA, PROGRAM, protein tertiary structure, whole-genome shotgun, Genomic library, DNA sequencing, chromosome map, Expressed Sequence Tags, 2. Zero hunger, Genetics, 0303 health sciences, Expressed sequence tag, industrial crops, Shotgun sequencing, Chromosome Mapping, High-Throughput Nucleotide Sequencing, food and beverages, methodology, Genome project, L, ALIGNMENT, Genome, Plant, Linen, Linum, DNA, Plant, DATABASE, flax, Molecular Sequence Data, DNA sequence, Crops, RNA GENES, Linum usitatissimum, ORGANIZATION, NUCLEAR-DNA CONTENT, Biology, chemistry, high throughput sequencing, 03 medical and health sciences, Illumina, Yarn, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, gene library, plant DNA, 030304 developmental biology, Whole genome sequencing, LTR RETROTRANSPOSONS, Base Sequence, Proteins, nucleotide sequence, Molecular Sequence Annotation, Sequence Analysis, DNA, Cell Biology, biology.organism_classification, Protein Structure, Tertiary, Genes, molecular genetics, bacterial artificial chromosome, AMINO-ACID-SEQUENCES, 010606 plant biology & botany
Abstract: Flax (Linum usitatissimum) is an ancient crop that is widely cultivated as a source of fiber, oil and medicinally relevant compounds. To accelerate crop improvement, we performed whole-genome shotgun sequencing of the nuclear genome of flax. Seven paired-end libraries ranging in size from 300 bp to 10 kb were sequenced using an Illumina genome analyzer. A de novo assembly, comprised exclusively of deep-coverage (approximately 94× raw, approximately 69× filtered) short-sequence reads (44-100 bp), produced a set of scaffolds with N50 = 694 kb, including contigs with N50 = 20.1 kb. The contig assembly contained 302 Mb of non-redundant sequence representing an estimated 81% genome coverage. Up to 96% of published flax ESTs aligned to the whole-genome shotgun scaffolds. However, comparisons with independently sequenced BACs and fosmids showed some mis-assembly of regions at the genome scale. A total of 43 384 protein-coding genes were predicted in the whole-genome shotgun assembly, and up to 93% of published flax ESTs, and 86% of A. thaliana genes aligned to these predicted genes, indicating excellent coverage and accuracy at the gene level. Analysis of the synonymous substitution rates (Ks) observed within duplicate gene pairs was consistent with a recent (5-9 MYA) whole-genome duplication in flax. Within the predicted proteome, we observed enrichment of many conserved domains (Pfam-A) that may contribute to the unique properties of this crop, including agglutinin proteins. Together these results show that de novo assembly, based solely on whole-genome shotgun short-sequence reads, is an efficient means of obtaining nearly complete genome sequence information for some plant species. © 2012 Blackwell Publishing Ltd.
Published: 2012
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30. A new Eriophyid, Aceria madhucae n. sp. (Acari: Eriophyidae) from India

Author: V. V. Ramamurthy, Sushila Joshi, Pratibha Menon, and Indian Agricultural Research Institute ( IARI )
Subjects: 0106 biological sciences, mites, Arthropoda, [ SDV.BA.ZI ] Life Sciences [q-bio]/Animal biology/Invertebrate Zoology, 010607 zoology, Madhuca longifolia, India, Eriophyidae, Acariformes, 01 natural sciences, Eriophyoidea, Arachnida, Botany, Animalia, Acari, Empodium, Taxonomy, new species, biology, Prostigmata, Seta, Biodiversity, biology.organism_classification, 010602 entomology, Insect Science, Aceria, [ SDV.BID.SPT ] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
Abstract: International audience; A new species of Eriophyidae (Acari: Prostigmata: Eriophyoidea), Aceria madhucae n. sp., collected from the lower surface of leaves of Madhuca longifolia (J. Konig) J.F. Macbr. (Sapotaceae), is described from India. Aceria madhucae is distinguished by its 4-rayed empodium, shield with median line present from rear to almost middle of shield, admedian lines, two pair of submedian lines and a pair of short lateral lines present, all parallel to median line and slightly diverging laterally, opisthosomal seta h1 absent, pedipalp genual seta d present.
Published: 2011
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31. Analysing and improving the role of grain legumes in cropping system’s sustainability: a system approach illustrated on chickpea in India and Europe

Author: Wery, Jacques, Ahlawat, I.P.S., UMR INRA / ENSAM / CIRAD : Fonctionnement et Conduite des Systèmes de Cultures Tropicaux et Méditerranéens, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Montpellier (ENSA M), Indian Agricultural Research Institute (IARI), and ProdInra, Migration
Subjects: [SDV] Life Sciences [q-bio], [SDE] Environmental Sciences, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, POIS CHICHE, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2006

32. Effects of grazing on microbial functional groups involved in soil N dynamics

Author: James I. Prosser, P. Loiseau, Ashok K. Patra, Valérie Degrange, A. Clays-Josserand, X. Le Roux, Shahid Mahmood, Sylvie Nazaret, Susan J. Grayston, Agnès Richaume, Franck Poly, Luc Abbadie, Laurent Philippot, Frédérique Louault, Laboratoire d'Ecologie Microbienne - UMR 5557 (LEM), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Ecole Nationale Vétérinaire de Lyon (ENVL)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Indian Agricultural Research Institute (IARI), Laboratoire Ecologie et évolution, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Macaulay Land Use Research Institute, Partenaires INRAE, Unité de recherche Agronomie de Clermont (URAC), Institut National de la Recherche Agronomique (INRA), University of Aberdeen, Microbiologie, Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Ecole Nationale Vétérinaire de Lyon (ENVL), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), UR 0874 Unité de recherche Agronomie de Clermont, Institut National de la Recherche Agronomique (INRA)-Environnement et Agronomie (E.A.)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), and Institut National de la Recherche Agronomique (INRA)-Unité de recherche Agronomie de Clermont (URAC)
Subjects: 0106 biological sciences, Denitrification, POLYMORPHISME DES FRAGMENTS DE RESTRICTION, MICROBIAL FUNCTIONAL COMMUNITIES, [SDV]Life Sciences [q-bio], Biology, NITRIFICATION, 010603 evolutionary biology, 01 natural sciences, Grassland, Grazing, NITROGEN CYCLE, Nitrogen cycle, Ecology, Evolution, Behavior and Systematics, 2. Zero hunger, geography, GRAZING, geography.geographical_feature_category, Ecology, FREE N-2 FIXATION, CARBON MINERALIZATION, DENITRIFICATION, 04 agricultural and veterinary sciences, Mineralization (soil science), 15. Life on land, Microbial population biology, 13. Climate action, [SDE]Environmental Sciences, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Nitrification, GRASSLANDS, MICROBIAL DIVERSITY
Abstract: International audience; Enhancement of soil nitrogen (N) cycling by grazing has been observed in many grassland ecosystems. However, whether grazing affects the activity only of the key microbial functional groups driving soil N dynamics or also affects the size (cell number) and/or composition of these groups remains largely unknown. We studied the enzyme activity, size, and composition of five soil microbial communities (total microbial and total bacterial communities, and three functional groups driving N dynamics: nitrifiers, denitrifiers, and free N-2 fixers) in grassland sites experiencing contrasting sheep grazing regimes (one light grazing [LG] site and one intensive grazing [IG] site) at two topographical locations. Enzyme activity was determined by potential carbon mineralization, nitrification, denitrification, and N-2 fixation assays. The size of each community (except N-2 fixers) was measured by the most-probable-number technique. The composition of the total soil microbial community was characterized by phospholipid fatty acid analysis (PLFA), and the genetic structure of the total bacterial community was assessed by ribosomal intergenic spacer analysis. The genetic structures of the ammonia-oxidizing, nitrate-reducing, and N-2- fixing communities were characterized by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) or by polymerase chain reaction and denaturing gradient gel electrophoresis (PCR-DGGE) targeting group-specific genes. Greater enzyme activities, particularly for nitrification, were observed in IG than in LG sites at both topographical locations. The numbers of heterotrophs, nitrifiers, and denitrifiers were higher in IG than in LG sites at both topographical locations. The amplitude of changes in community size was higher than that of community enzyme activity. Phospholipid and nucleic acid analyses showed that the composition/structure of all the communities, except nitrate reducers, differed between IG and LG sites at both locations. For each community, changes in activity were correlated with changes in the occurrence of a few individual PLFAs or DNA fragments. Our results thus indicate that grazing enhances the activity of soil microbial communities but also concurrently induces changes in the size and composition/structure of these communities on the sites studied. Although the generality of our conclusions should be tested in other systems, these results are of major importance for predicting the effects of future disturbances or changed grazing regimes on the functioning of grazed ecosystems.
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33. Residual correlation and ensemble modelling to improve crop and grassland models

Author: Renáta Sándor, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, Jean-François Soussana, Bruno Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher D. Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Tom Harrison, Ute Skiba, Miko U.F. Kirschbaum, Katja Klumpp, Patricia Laville, Joel Léonard, Raphaël Martin, Raia Silvia Massad, Andrew D. Moore, Vasileios Myrgiotis, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ward Smith, Lianhai Wu, Qing Zhang, Gianni Bellocchi, Producció Vegetal, Cultius Extensius Sostenibles, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agricultural Institute (ELKH CAR), Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), RITTMO Agroenvironnement (RITTMO), Queensland University of Technology [Brisbane] (QUT), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, AgResearch, Lincoln Res Ctr, PB 4749, Christchurch 8140, New Zealand, Partenaires INRAE, Indian Agricultural Research Institute (IARI), Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze = University of Florence (UniFI), Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Desertification Research Group, Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Texas A and M AgriLife Research, Texas A&M University System, Canadian National Collection of Insects, Arachnids, and Nematodes, Science & Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON, K1A 0C6, Agriculture and Agri-Food (AAFC), University of Tasmania [Launceston] (UTAS), NERC Centre for Ecology and Hydrology, Penicuik, UK, Manaaki Whenua – Landcare Research [New Zealand], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), CSIRO Agriculture and Food (CSIRO), School of Geosciences [Edinburgh], University of Edinburgh, Potsdam Institute for Climate Impact Research (PIK), The New Zealand Institute for Plant and Food Research Limited (PFR), Mt. Albert Research Centre, Sustainable Agriculture Systems (Rothamsted Research), Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics [Beijing] (IAP), and Chinese Academy of Sciences [Beijing] (CAS)-Chinese Academy of Sciences [Beijing] (CAS)
Subjects: Environmental Engineering, Correlation matrices, Ecological Modeling, [SDE]Environmental Sciences, Ensemble modelling, Software, Biogeochemical models
Abstract: Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development. info:eu-repo/semantics/acceptedVersion
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34. Rising temperatures reduce global wheat production

Author: Margarita Garcia-Vila, L. A. Hunt, Jørgen E. Olesen, P. V. V. Prasad, Pierre Martre, Katharina Waha, Curtis D. Jones, Pramod K. Aggarwal, Yan Zhu, Phillip D. Alderman, Christian Biernath, Fulu Tao, Bruno Basso, Davide Cammarano, Christoph Müller, Eckart Priesack, Taru Palosuo, Mohamed Jabloun, Thilo Streck, Zhigan Zhao, Jordi Doltra, Roberto C. Izaurralde, Alex C. Ruane, Andrew J. Challinor, Michael J. Ottman, Jeffrey W. White, Garry O'Leary, Reimund P. Rötter, David B. Lobell, Sebastian Gayler, Gerard W. Wall, G. De Sanctis, Matthew P. Reynolds, Senthold Asseng, Iurii Shcherbak, Peter J. Thorburn, Enli Wang, Bruce A. Kimball, Daniel Wallach, Mikhail A. Semenov, Claudio O. Stöckle, Claas Nendel, Jakarat Anothai, Ehsan Eyshi Rezaei, Pierre Stratonovitch, Ann-Kristin Koehler, Joost Wolf, Kurt Christian Kersebaum, Gerrit Hoogenboom, Frank Ewert, Iwan Supit, S. Naresh Kumar, Elias Fereres, NASA's Goddard Space Flight Center, Columbia University, University of Florida, Department of Agriculture (US), Oregon State University, International Maize and Wheat Improvement Center, University of Agriculture Faisalabad, Shahid Beheshti University, ARVALIS, International Food Policy Research Institute (US), Federal Ministry of Education and Research (Germany), German Research Foundation, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, CGIAR (France), Grains Research and Development Corporation (Australia), Department of Environment and Primary Industries (Australia), Texas AgriLife Research, Texas A&M University, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Plant Production Research, Agrifood Research Finland, Stanford University, Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Department of Agronomy, Purdue University [West Lafayette], CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, University of Leeds, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Córdoba [Cordoba], WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Biological Systems Engineering, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Institute of Landscape System Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of landscape systems analysis, Department of Environment and Primary Industries, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Department of Geological Sciences, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Computational and Systems Biology, John Innes Centre [Norwich], Institute of Soil Science and Land Evaluation, University of Hohenheim, Plant Production Systems and Earth System Science, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Agriculture Flagship, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Department of Agronomy and Biotechnology, China Agricultural University (CAU), Nanjing Agricultural University, International Food Policy Research Institute (IFPRI), USDA National Institute for Food and Agriculture [32011-68002-30191], KULUNDA [01LL0905L], FACCE MACSUR project through the German FederalMinistry of Education and Research (BMBF) [031A103B, 2812ERA115], German Science Foundation [EW119/5-1], FACCEMACSUR project by the Danish Strategic Research Council, FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL), FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China [41071030], Helmholtz project 'REKLIM-Regional Climate Change: Causes and Effects' Topic 9: 'Climate Change and Air Quality', CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), Australian Grains Research and Development Corporation, Department of Environment and Primary Industries Victoria, Australia, Texas AgriLife Research, Texas AM University, CSIRO, Chinese Academy of Sciences (CAS), Helmholtz Zentrum München = German Research Center for Environmental Health, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Université de Toulouse (UT)-Université de Toulouse (UT), and Nanjing Agricultural University (NAU)
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Growing season, Climate change, Environmental Science (miscellaneous), Atmospheric sciences, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Production (economics), 0105 earth and related environmental sciences, 2. Zero hunger, business.industry, Global warming, Simulation modeling, 15. Life on land, Agronomy, 13. Climate action, Agriculture, [SDE]Environmental Sciences, Environmental science, business, Social Sciences (miscellaneous), 010606 plant biology & botany
Abstract: Asseng, S. et al., Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time., We thank the Agricultural Model Intercomparison and Improvement Project and its leaders C. Rosenzweig from NASA Goddard Institute for Space Studies and Columbia University (USA), J. Jones from University of Florida (USA), J. Hatfield from United States Department of Agriculture (USA) and J. Antle from Oregon State University (USA) for support. We also thank M. Lopez from CIMMYT (Turkey), M. Usman Bashir from University of Agriculture, Faisalabad (Pakistan), S. Soufizadeh from Shahid Beheshti University (Iran), and J. Lorgeou and J-C. Deswarte from ARVALIS—Institut du Végétal (France) for assistance with selecting key locations and quantifying regional crop cultivars, anthesis and maturity dates and R. Raymundo for assistance with GIS. S.A. and D.C. received financial support from the International Food Policy Research Institute (IFPRI). C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Science Foundation (project EW 119/5-1). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM—Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and P.D.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O’L. was funded through the Australian Grains Research and Development Corporation and the Department of Environment and Primary Industries Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. E.W. and Z.Z. were funded by CSIRO and the Chinese Academy of Sciences (CAS) through the research project ‘Advancing crop yield while reducing the use of water and nitrogen’ and by the CSIRO-MoE PhD Research Program.
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35. Elevating the rice blast disease immunity through CPKA protein targeting in Magnaporthe oryzae ( M. oryzae ) with natural compounds.

Author: Mahanandia NC, Biswal S, Mishra DC, Srivastava S, Chaturvedi KK, Murmu S, Sharma A, Jha GK, and Farooqi MS
Abstract: Rice blast disease, instigated by Magnaporthe oryzae ( M. oryzae ), significantly impedes global rice production. Targeting the M. oryzae signaling protein, cAMP-Protein Kinase A (CPKA), which facilitates appressorium development and host penetration, this study explores the potential inhibitory effects of natural compounds. Virtual screening, molecular docking and text mining approaches were used to find the nimonol and curcumin that inhibit the CPKA protein. The MM-PBSA method was used to do the molecular dynamics (MD) simulation and the binding free energy analysis of the molecule. Their binding free energies (ΔG bind ) of -78.81 kJ/mol and -117.65 kJ/mol, respectively, point to the potential efficacy of the CPKA-nimonol and CPKA-curcumin complexes as inhibitors. The inhibitory effects have been further verified by various MD analysis. This study emphasizes the need to conduct further study on the CPKA protein and gives important insights into the possibility of natural compounds as M. oryzae inhibitors.
Published: 2025
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36. Phyto-nutraceutical promise of Brassica vegetables in post-genomic era: a comprehensive review.

Author: Singh S, Das A, Singh R, Chikh-Rouhou H, Priyadarsini S, and Nandi A
Subjects: Humans, Vegetables genetics, Genomics, Phytochemicals, Functional Food, Brassica genetics, Brassica metabolism, Dietary Supplements
Abstract: Main Conclusion: Brassica vegetables are one of the possible solutions to tackle the emerging human diseases and malnutrition due to their rich content of phyto-nutraceutaical compounds. The genomics enabled tools have facilitated the elucidation of molecular regulation, mapping of genes/QTLs governing nutraceutical compounds, and development of nutrient-rich Brassica vegetables. The enriched food products or foods as whole termed as functional foods are intended to provide health benefits. The 2500 year old Hippocratic phrase 'let thy food be thy medicine and thy medicine be thy food' remained in anonymity due to lack of sufficient evidence. However, today, we are facing reappraisal of healthy nutritious functional foods in battling diseases. In this context, the Brassica vegetables represent the most extensively investigated class of functional foods. An optimal consumption of Brassica vegetables is associated with lowering the risks of several types of cancer, chronic diseases, cardiovascular disease, and help in autism. In the post-genomic era, the integration of genetic and neoteric omics tools like transcriptomics, metabolomics, and proteomics have illuminated the downstream genetic mechanisms governing functional food value of Brassica vegetables. In this review, we have summarized in brief the phyto-nutraceutical profile and their functionality in Brassica vegetables. This review also highlights the progress made in identification of candidate genes/QTLs for accumulation of bioactive compounds in Brassica vegetables. We summarize the molecular regulation of major phytochemicals and breeding triumphs in delivering multifunctional Brassica vegetables., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflict of interest., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Published: 2024
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37. Relevance of Indian Traditional Herbal Brews for Gut Microbiota Balance.

Author: Joshi DD, Deb L, Kaul K, Somkuwar BG, Rana VS, and Singh R
Abstract: The considerable changes in lifestyle patterns primarily affect the human gut microbiota and result in obesity, diabetes, dyslipidemia, renal complications, etc. though there are few traditional safeguards such as herbal brews to maintain the ecological stability under intestinal dysbiosis. The present article is designed to collect all the scientific facts in a place to decipher the role of the Indian traditional herbal brews used to balance gut health for centuries. Computerized databases, commercial search engines, research papers, articles, and books were used to search by using different keywords to select the most appropriate published articles from 2000 onward to September 2023. A total of 1907 articles were scrutinized, 46 articles were finally selected from the 254 screened, and targeted information was compiled. Interaction of herbal brews to the gut microflora and resulting metabolites act as prebiotics due to antimicrobial, anti-inflammatory, and antioxidant properties, and modulate the pH of the gut. The effect of brews on gut microbiota has a drastic impact on various gut-related diseases and has gained popularity as an alternative to antibiotics against bacteria, fungi, viruses, parasites, and boosting the immune system and strengthening the intestinal barrier. Berberine, kaempferol, piperine, and quercetin have been found in more than one brew discussed in the present article. Practically, these brews balance the gut microbiota, prevent chronic and degenerative diseases, and reduce organ inflammation, though, there is a knowledge gap on the molecular mechanism to explain their efficacy. Indian traditional herbal brews used to reboot and heal the gut microbiota since centuries-old practice with successful history without toxicity. The systematic consumption of these brews under specific dietary prescriptions has a hope of arrays for a healthy human gut microbiome in the present hasty lifestyle with overall health and well-being., Competing Interests: Conflict of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.)
Published: 2024
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38. Arsenic Stress Mitigation Using a Novel Plant Growth-Promoting Bacterial Strain Bacillus mycoides NR5 in Spinach Plant (Spinacia oleracea L.).

Author: Sarim KM, Shukla R, Bhoyar MS, Kaur B, and Singh DP
Subjects: India, Plant Roots microbiology, Soil Microbiology, Stress, Physiological, Soil Pollutants metabolism, Proline metabolism, Anti-Bacterial Agents pharmacology, Arsenic metabolism, Bacillus metabolism, Bacillus genetics, Bacillus isolation & purification, Spinacia oleracea microbiology, Biodegradation, Environmental
Abstract: Present study aimed to identify arsenic (As)-resistant bacterial strains that can be used to mitigate arsenic stress. A bacterium Bacillus mycoides NR5 having As tolerance limit of 1100 mg L -1 was isolated from Nag River, Maharashtra, India. It was also equipped with plant growth-promoting (PGP) attributes like phosphate solubilization, siderophores, ammonia, and nitrate reduction, with added antibiotic tolerance. Furthermore, scanning electron microscopy (SEM) and transmission electron micrograph (TEM) suggested biosorption as possible mechanisms of arsenic tolerance. A strong peak in FTIR spectra at 3379.0 corresponding to amine in As-treated NR5 also indicated metal interaction with cell surface protein. Amplification of arsenic reductase gene in NR5 further suggested intracellular transformation of As speciation. Moreover, As tolerance capability of NR5 was shown in spinach plants in which the bacterium effectively mitigated 25 ppm As by producing defense-related proline molecules. Evidence from SEM, TEM, and FTIR, concluded biosorption possibly the primary mechanism of As tolerance in NR5 along with the transformation of arsenic. B. mycoides NR5 with PGP attributes, high As tolerance, and antibiotic resistance mediated enhanced As tolerance in spinach plants advocated that the strain can be a better choice for As bioremediation in contaminated agricultural soil and water., (© 2024 Wiley‐VCH GmbH.)
Published: 2024
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39. Training effectiveness evaluation: Advancing a Kirkpatrick model based composite framework.

Author: Paul S, Burman RR, and Singh R
Subjects: Humans, India, Female, Cross-Sectional Studies, Farmers, Male, Agriculture education, Program Evaluation methods, Rural Population
Abstract: The present study aims at ideating a quantitative protocol to evaluate effectiveness of training programmes especially meant for farmers, farm women, and rural youth. The specific objective was to develop a robust framework for measuring the effectiveness of rural training programmes organized by the Farmers Training Centres (FTCs) of India. Kirkpatrick's training evaluation model provides us the foundation to design a four-dimensional composite framework based on range-based indicator normalization, principal component analysis based indicator weight estimation, and rank correlation based framework sensitivity testing. We used cross-sectional primary data generated through household survey and personal interviews with randomly selected one thousand trainees to test and validate our proposed protocol. Applying it on our evaluation target we find that the degree of effectiveness of the training programmes varies; one in every four training programmes may not be effective. Trainees' reactions on various aspects of the training programmes may have positive and significant influence on learning. Training outcomes may be linked with the trainees' post-training changes in behaviour. A sensitivity test confirms that the proposed framework is not susceptible to changes in weighting schemes, implying robustness of indicator selection. The findings offer dissection of individual training programmes guiding policy decisions for a training organization. The proposed framework enriches the Kirkpatrick's training evaluation model by offering standardized indicators for training effectiveness evaluation., Competing Interests: Declarations of Competing interest We, the authors of the present article do hereby declare that we have no financial and personal relationships with other people or organizations that could inappropriately influence or bias the present work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
Published: 2024
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40. Synthesis and Evaluation of Starch-Grafted-Poly[(Acrylic Acid)- Co -Acrylamide] Based Nanoclay Polymer Composite Fertilizers for Slow Release of Nitrogen in Soil.

Author: Saini R, Manjaiah KM, Roy D, Kumar R, Gawdiya S, Meena S, Naveenkumar A, Kumar A, El-Hendawy S, and Mattar MA
Abstract: Nitrogen (N) losses from conventional N fertilizers contribute to environmental degradation and low N use efficiency. Highlighting the need for slow-release fertilizers (SRFs) to mitigate these problems, this study aims to develop slow-release N fertilizers using starch-grafted-poly[(acrylic acid)- co -acrylamide] based nanoclay polymer composites (NCPCs) and investigate their efficacy for slow N delivery in soil. Three types of NCPCs, NCPC(A) (poly [(acrylic acid)- co -acrylamide]), NCPC(W) (wheat starch-grafted-poly[(acrylic acid)- co -acrylamide), and NCPC(M) (maize starch-grafted-poly[(acrylic acid)- co -acrylamide) were prepared and characterized using FTIR spectroscopy and X-ray diffraction techniques. N-release behaviour of the products was assessed under two distinct soils, i.e., Assam (Typic Hapludults, pH 4.2) and Delhi (Typic Haplustepts, pH 7.9) soils. Additionally, the effects of varying soil moisture and temperature levels on N release were studied in the Assam soil. The N-release kinetics of the synthesized fertilizers were assessed using zero-order, first-order, Higuchi, and Korsmeyer-Peppas models. Degradability of the NCPCs was evaluated by measuring evolved CO 2 -C under various soil conditions as an indicator of microbial degradation. The results indicated that NCPC fertilizers significantly slowed down the release of N compared to urea. According to the R 2 values obtained, it was evident that the first-order kinetic model most accurately describes the N release from both urea and NCPC-based N fertilizers in the studied soils. Among the formulations, NCPC(A) exhibited the lowest N release (42.94-53.76%), followed by NCPC(M) (51.05-61.70%), NCPC(W) (54.86-67.75%), and urea (74.33-84.27%) after 21 days of incubation. The rate of N release was lower in the Assam soil compared to the Delhi soil, with higher soil moisture and temperature levels accelerating the release. Starch addition improved the biodegradability of the NCPCs, with NCPC(W) showing the highest cumulative CO 2 -C evolution (18.18-22.62 mg g -1 ), followed by NCPC(M) (15.54-20.97 mg g -1 ) and NCPC(A) (10.89-19.53 mg g -1 ). In conclusion, NCPC-based slow-release fertilizers demonstrated a more gradual N release compared to conventional urea and the inclusion of starch enhanced their degradability in the soil, which confirms their potential for sustainable agricultural applications. However, soil properties and environmental factors influenced the N release and degradation rates of NCPCs.
Published: 2024
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41. Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges.

Author: Sharma R, Nath PC, Lodh BK, Mukherjee J, Mahata N, Gopikrishna K, Tiwari ON, and Bhunia B
Subjects: Fraud prevention & control, Food Analysis methods, Food Safety, Mass Spectrometry, Food Contamination analysis
Abstract: Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
Published: 2024
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42. Cytokinin oxidase2-deficient mutants improve panicle and grain architecture through cytokinin accumulation and enhance drought tolerance in indica rice.

Author: Rashid A, Achary VMM, Abdin MZ, Karippadakam S, Parmar H, Panditi V, Prakash G, Bhatnagar-Mathur P, and Reddy MK
Subjects: Edible Grain genetics, Edible Grain growth & development, Gene Expression Regulation, Plant, Mutation genetics, Photosynthesis genetics, Plants, Genetically Modified, Stress, Physiological genetics, Cytokinins metabolism, Drought Resistance genetics, Oryza genetics, Oryza physiology, Oryza growth & development, Oryza enzymology, Oxidoreductases genetics, Oxidoreductases metabolism, Plant Proteins genetics, Plant Proteins metabolism
Abstract: Key Message: The Osckx2 mutant accumulates cytokinin thereby enhancing panicle branching, grain yield, and drought tolerance, marked by improved survival rate, membrane integrity, and photosynthetic function. Cytokinins (CKs) are multifaceted hormones that regulate growth, development, and stress responses in plants. Cytokinins have been implicated in improved panicle architecture and grain yield; however, they are inactivated by the enzyme cytokinin oxidase (CKX). In this study, we developed a cytokinin oxidase 2 (Osckx2)-deficient mutant using CRISPR/Cas9 gene editing in indica rice and assessed its function under water-deficit and salinity conditions. Loss of OsCKX2 function increased grain number, secondary panicle branching, and overall grain yield through improved cytokinin content in the panicle tissue. Under drought conditions, the Osckx2 mutant conserved more water and demonstrated improved water-saving traits. Through reduced transpiration, Osckx2 mutants showed an improved survival response than the wild type to unset dehydration stress. Further, Osckx2 maintained chloroplast and membrane integrity and showed significantly improved photosynthetic function under drought conditions through enhanced antioxidant protection systems. The OsCKX2 function negatively affects panicle grain number and drought tolerance, with no discernible impact in response to salinity. The finding suggests the utility of the beneficial Osckx2 allele in breeding to develop climate-resilient, high-yielding cultivars for future food security., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Published: 2024
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43. Stem traits promote wheat climate-resilience.

Author: Ntawuguranayo S, Zilberberg M, Nashef K, Bonfil DJ, Bainsla NK, Piñera-Chavez FJ, Reynolds MP, Peleg Z, and Ben-David R
Abstract: Introduction: Wheat grain filling processes under post-anthesis stress scenarios depend mainly on stem traits and remobilization of stem water-soluble carbohydrates (WSC)., Methods: A diverse panel of advanced semi-dwarf spring wheat lines, representing a natural variation in stem traits (WSC content, stem diameter, peduncle length, and stem wall width), was used to identify specific traits that reliably reflect the relationship between WSC and grain yield. The panel was phenotyped under various environmental conditions: well-watered, water-limited, and heat stress in Mexico, and terminal-drought in Israel., Results: Environmental stresses reduced grain yield (from 626 g m -2 under well-watered to 213 g m -2 under heat), lower internode diameter, and peduncle length. However, stem-WSC generally peaked 3-4 weeks after heading under all environmental conditions except heat (where it peaked earlier) and expressed the highest values under water-limited and terminal-drought environments. Increased investment in internode diameter and peduncle length was associated with a higher accumulation of stem WSC, which showed a positive association with yield and kernel weight. Across all environments, there were no apparent trade-offs between increased crop investment in internode diameter, peduncle length, and grain yield., Discussion: Our results showed that selecting for genotypes with higher resource investment in stem structural biomass, WSC accumulation, and remobilization could be a valuable strategy to ameliorate grain size reduction under stress without compromising grain yield potential. Furthermore, easy-to-measure proxies for WSC (stem diameter at specific internodes and length of the last internode, i.e., the peduncle) could significantly increase throughput, potentially at the breeding scale., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor GS declared a past co-authorship with the authors MR, DB, and RB-D., (Copyright © 2024 Ntawuguranayo, Zilberberg, Nashef, Bonfil, Bainsla, Piñera-Chavez, Reynolds, Peleg and Ben-David.)
Published: 2024
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44. Author Correction: Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea.

Author: Khan AW, Garg V, Sun S, Gupta S, Dudchenko O, Roorkiwal M, Chitikineni A, Bayer PE, Shi C, Upadhyaya HD, Bohra A, Bharadwaj C, Mir RR, Baruch K, Yang B, Coyne CJ, Bansal KC, Nguyen HT, Ronen G, Aiden EL, Veneklaas E, Siddique KHM, Liu X, Edwards D, and Varshney RK
Published: 2024
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45. Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea.

Author: Khan AW, Garg V, Sun S, Gupta S, Dudchenko O, Roorkiwal M, Chitikineni A, Bayer PE, Shi C, Upadhyaya HD, Bohra A, Bharadwaj C, Mir RR, Baruch K, Yang B, Coyne CJ, Bansal KC, Nguyen HT, Ronen G, Aiden EL, Veneklaas E, Siddique KHM, Liu X, Edwards D, and Varshney RK
Subjects: Genetic Variation, Evolution, Molecular, Plant Breeding methods, Phylogeny, Phenotype, Cicer genetics, Genome, Plant, Quantitative Trait Loci, Crops, Agricultural genetics
Abstract: Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2024
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46. Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system.

Author: Kumar K, Parihar CM, Nayak HS, Sena DR, Godara S, Dhakar R, Patra K, Sarkar A, Bharadwaj S, Ghasal PC, L Meena A, Reddy KS, Das TK, Jat SL, Sharma DK, Saharawat YS, Singh U, Jat ML, and Gathala MK
Subjects: Triticum growth & development, Triticum metabolism, Crops, Agricultural growth & development, Biomass, Zea mays growth & development, Zea mays metabolism, Nitrogen metabolism, Agriculture methods, Fertilizers, Soil chemistry
Abstract: Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar's anthesis and physiological maturity, with observed value falling within 5% of the model's predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9-16%. The study also demonstrated the model's ability to accurately capture soil nitrate-N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop's growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further., (© 2024. The Author(s).)
Published: 2024
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47. Functional foods with a tailored glycemic response based on food matrix and its interactions: Can it be a reality?

Author: Mondal D, Awana M, Mandal S, Pandit K, Singh A, Syeunda CO, Thandapilly SJ, and Krishnan V
Abstract: Functional foods are considered the future of nutrition because they benefit human health and environmental sustainability. They offer natural solutions for managing post-prandial glycemia and its long-term consequences. Therefore, understanding the composition and inherent dynamics of the functional food matrix (FM) is crucial. Within the FM, components like proteins, fats, carbohydrates, phenolic compounds, fibres, and minor elements interact dynamically, highlighting how individual components within the system behave. This review highlights the significance of diverse FM interactions in modulating inherent glycemic potential (IGP). These interactions comprise major binary, ternary, quaternary interactions, and minor interactions, in contemporary functional food formulations that include starch-derived additives, biopeptides, and flavouring agents. The starch quality matrix (SQM), a prediction model for customised functional foods with low IGP, has been briefed as a pilot concept. We also investigate the impact of these interactions on gut health, fill in the knowledge gaps, and provide recommendations for further study., Competing Interests: The authors confirm that there are no known conflicts of interest associated with this work., (© 2024 The Authors.)
Published: 2024
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48. Autofluorescence-spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.

Author: Rajendran A, Ramlal A, Sarkar S, Agasti SS, Rajarajan K, Lal SK, Raju D, and Subramaniam S
Abstract: The autofluorescence-spectral imaging (ASI) technique is based on the light-emitting ability of natural fluorophores. Soybean genotypes showing contrasting tolerance to pre-germination anaerobic stress can be characterized using the photon absorption and fluorescence emission of natural fluorophores occurring in seed coats. In this study, tolerant seeds were efficiently distinguished from susceptible genotypes at 405 nm and 638 nm excitation wavelengths. ASI approach can be employed as a new marker for the detection of photon-emitting compounds in the tolerant and susceptible soybean seed coats. Furthermore, the accuracy of rapid characterization of genotypes using this technique can provide novel insights into soybean breeding., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Rajendran, Ramlal, Sarkar, Agasti, Rajarajan, Lal, Raju and Subramaniam.)
Published: 2024
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49. Metabolite profiling of plant growth promoting cyanobacteria- Anabaena laxa and Calothrix elenkinii, using untargeted metabolomics.

Author: Nishanth S, Kokila V, and Prasanna R
Abstract: The metabolite profiles of two plant growth promoting cyanobacteria- Anabaena laxa and Calothrix elenkinii , which serve as promising biofertilizers, and biocontrol agents were generated to investigate their agriculturally beneficial activities. Preliminary biochemical analyses, in terms of total chlorophyll, total proteins, and IAA were highest at 14 days after inoculation (DAI). In A. laxa 20-25% higher values of reducing sugars, than C. elenkinii at both 14 and 21 DAI were recorded. Carbon and nitrogen assimilating enzyme activities-phosphoenol pyruvate carboxylase (PEPC), carbonic anhydrase (CA), and glutamine synthetase (GS) were highest at 14 DAI, albeit, nitrate reductase (NR) activity was higher by 0.73-0.84-fold at 21 DAI. Untargeted GC-MS (Gas chromatography-Mass spectrometric) analysis of metabolite profiles of 21d-old cyanobacterial cultures and characterization using NIST mass spectral library illustrated that A. laxa recorded highest number of metabolite hits in three chemical classes namely amino acid and peptides, nucleotides, nucleosides and analogues, besides other organic compounds. Based on the pathway analysis of identified metabolites, both A. laxa , and C. elenkinii were enriched in metabolites involved in aminoacyl-tRNA biosynthesis, and amino acid metabolism pathways, particularly lactose and glutamic acid, which are important players in plant-microbe interactions. Correlation-based metabolite network illustrated distinct and significant differences in the metabolic machinery of A. laxa and C. elenkinii , highlighting their novel identity and enrichment in C-N rich metabolites, as factors underlying their plant growth and soil fertility enhancing attributes, which make them valuable as inoculants., Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03902-7., Competing Interests: Conflict of interestThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors report no commercial or proprietary interest in any product or concept discussed in this article., (© King Abdulaziz City for Science and Technology 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Published: 2024
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50. A novel method for detecting conformation-dependent β-glucans-single, double, and triple-helical from Shiitake mushroom.

Author: Chakraborty S, Beura M, Sarda Devi K, Ratankumar Singh A, Keerthana CS, Sharma SK, Singh A, Dahuja A, and Krishnan V
Abstract: This pioneering study explores the structural intricacies of therapeutic β-glucan in Shiitake ( Lentinula edodes ), i.e. Lentinan (LNT). Lentinan, a neutral polysaccharide [β-(1,3; 1,6) glucan], exists in three forms; single, double, and triple-helical, but conformation-dependent bioactivity studies are lacking. In this context, we meticulously assessed indigenous Shiitake accessions from Northeast India, unveiling the conformational spectrum of LNT through an innovative pipeline. The experiment approached the simultaneous estimation of total glucan (TG), triple helical glucan (THG), and single-double helical glucan (SDG). Profiling revealed the exceptional LNT content in DMRO-623 (TG: 46.74%, SDG: 9.34%, THG: 37.39%) which emerged as the highest documented to date. Beyond the culinary delight, this research and the novel approach to LNT quantification will create a pivotal platform for advanced mushroom research, offering prospects for novel discoveries, innovative applications, and therapeutic potential.
Published: 2024
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